Question: Optimization of Features and Root Exploit Detection: A Blockchain and Mobile Device Approach for Medical Data Management

This paper explores the forensic extraction of network packets and related data structures from system memory in popular operating systems like Windows, Linux, and OSX. We demonstrate that remnants of IP packets, Ethernet frames, and associated data structures often linger in memory even after network traffic has ceased, which can be crucial for forensic investigations. This residual data can help in reconstructing previous network activities, identifying connected devices on local or wireless networks, pinpointing the geographical location of the host system, and conducting cross-drive analysis. Additionally, we reveal that network data structures can be retrieved from memory that has been swapped to or persisted on a storage device during system hibernation or swapping. We outline our network carving methodologies, algorithms, and tools, and validate them using custom memory images and a publicly available forensic dataset. These techniques are particularly beneficial for forensic analysis of mobile devices and malware investigation, contributing significantly to cyber-security efforts. Published by Elsevier Ltd.

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from fine-grained user profiling to device security and network optimization). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: 1) the goal of the analysis; 2) the point where the network traffic is captured; and 3) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi access points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges, and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.

In this paper we describe a practical digital identity project of a global scale, which solves a number of privacy and scalability problems using the concepts of anonymous credentials and permissioned blockchains. Even though both ideas have been known and developed for a number of years, our system appears to be the first amalgamation. The prototype has been developed, tested, and published as open source. I. DIGITAL IDENTITY: HISTORY AND CHALLENGES The idea of digital identity can be traced to the beginning of the computer era, when the user profiles were first transferred across networks. In the real world, different authorities issue licenses, passports, identification cards, credit cards, checks, etc., generally called credentials, to ordinary users for their presentation to various service or goods providers. This brings the concept of Issuer, User, and Verifier as subjects of the identity system. It immediately became clear that issuance, storage, and presentation procedures must follow a number of security requirements to be competitive with the traditional paper credentials: • Compatibility. Credentials issued by different Issuers can be combined for a single presentation by User who is required to prove several properties simultaneously: e.g. that he is at least age 18 employed by XYZ Corp. with a driver license and good eyesight (does not require corrective lenses). • Unforgeability. A malicious User is unable to present a credential not issued by a legitimate Issuer. • Scalability. A system must handle hundreds of Issuers and billions of Users, such as an international passport system. It might be required to handle thousands of transactions per second like the Visa system [1]. • Performance/Low latency. Verification of credentials must be almost instant and not require broadband network connectivity per credential, even if it is issued by a previously unknown Issuer. Credentials can be stored and processed easily by the User even if he is offline. • Revocation. An Issuer can revoke any credential such that a User and any Verifiers can know within a reasonable amount of time that credential was revoked. Additionally, a digital identity system could provide features impossible or difficult to implement in a paper identity system: • Minimal dependencies. Aside from an earlier issuance, an Issuer should not be involved during the preparation and presentation of proofs, and the verification of credentials, including proof of nonrevocation. • Privacy/Anonymity. The real identity of a User can be kept secret from Verifiers who do not need it. • Unlinkability. Different presentations of the same credential cannot be linked together. • Selective disclosure. Any subset of attributes embedded in a credential can be kept secret from Verifiers. The latter group of properties is particularly attractive for Users from authoritarian countries or those who are concerned about leaks of their data from careless Issuers or Verifiers. The privacy-oriented digital identity schemes have been proposed as U-Prove [2] (mainly Microsoft, primarily based on Brands’ credentials [3]) and Idemix [4] (mainly IBM, primarily based on the Camenisch-Lysyanskaya credentials [5]), with the ABC4Trust and FIDO [6] projects encompassing these two. Despite significant effort expended on both projects, neither is in widespread use today. We deduce that all these systems lack compatibility and scalability, as an actual implementation would require a meta-system that congregates multiple Issuers and Verifiers and manages credential schemas. Before recently, all such system would require a (trusted) global third party for the exchange and distribution of Issuers’ data and parameters. It seems that such a centralized party on a global scale could not exist. II. OUR CONTRIBUTIONS We designed and implemented a system called Sovrin, which integrates anonymous credentials with revocation [7], [8] for privacy, unforgeability, performance, unlinkability and a distributed ledger, taking best practices from Ethereum [9] and BFT protocols. The implementation is mostly finished and will go live in the near future. A. Anonymous credentials for privacy It is well known that the anonymity and unlinkability properties are provided by various kinds of anonymous credentials, the concept dating back to seminal works by David Chaum in 1980s [10] and refined in a number of follow-up papers including well-known [3], [7], [11]. Zeroknowledge proofs allow a User to prove the possession of a credential without showing the credential itself, thus providing unlinkability. Additional features include delegation [11] and revocation [8]. We reference the Idemix specification [4], [12] as the basis for our anonymous credential module as it provides unlinkability by default (in contrast to U-Prove). Although Idemix is available as a Java library, for portability and fast prototyping we select the Charm framework [13], which provides a Python API for large integers, signature schemes, and pairings. It also allows us to integrate the pairing-based revocation module easily. B. Our treatment of the revocation feature The revocation feature was missing in the original papers behind U-Prove and Idemix and was added later in different ways. The revocation procedure assumes that each credential has special Revocation-ID attribute with value iR, and Issuer can at any moment revoke a particular iR. For Verifiers this means that if a User with a revoked iR presents his credential, this can be detected by the Verifier. We observe that in addition to various drawbacks described above, the revocation feature provides quite a privacy leak. Indeed, consider a typical attack setting: a Verifier colludes with an Issuer to disclose the identity of a User. The Issuer then revokes half of the issued credentials, and the Verifier asks the User to update his revocation data. If the User succeeds in presenting a valid non-revocation proof, he is among the users with non-revoked iR. Then the Issuer reinstates the earlier revocations and revokes another group of revocation IDs of equal size. Repeating this procedure logN times, the Issuer-Verifier team can uniquely identify one of N users. C. Our selection of revocation methods Since 2001, the following revocation methods have been proposed: • Signature lists [14]. Issuer regularly releases a list of signatures, one per non-revoked iR. User proves that his credential has the same ID as in some signature. Verification is fast, but the amount of data retrieved by User and computation he does is huge, and multiple presentations are linkable. • Cryptographic accumulators [7], [8]. The issued iR is added to (or removed from) a constant-size accumulator A, and a User must prove that his (non-disclosed) ID is still contained in A. This approach is reasonably fast for all parties. • Forward revocation lists [15]. For each revoked iR, an Issuer generates a revocation entry f(iR). A User presents a non-revocation proof to a Verifier, who then tests the proof against every entry in the list. If all matches fail, the credential is deemed valid. This method requires significant computational resources from the Verifier. We decided to use accumulators based on bilinear maps [8] for performance reasons. The small order of the target group allows for short accumulators and witnesses. We have our own implementation of accumulators, which is based on a corrected version of paper [8]. The drawback is that the User must be aware of all revoked credentials, as the proof is updated every time another credential is revoked and needs (public) Issuer-specific data for the update. If User has small capacity, the data he needs to update the nonrevocation proof reveals his ID. Existing research papers suggest User connecting to Issuer every time before creating a non-revocation proof [8], which is privacy-vulnerable if Issuer or his agent colludes with Verifier. D. Revocation with attribute-based sharding. The accumulator-based revocation procedure requires the User to maintain his non-revocation data w using the indices {iR} of the credentials revoked and issued from the last update. To update w the User also needs to access a global set G = {gi} of validity tails, published by Issuer. The tail set G is immutable but should be generated in advance and occupy quite big space (256 bytes per credential). Even though the number of tails needed for the update is proportional to the number of revoked credentials in the period, the tails retrieved by the User effectively disclose his own index iR and thus his identity. For a global Issuer the User would be unable to carry the entire set of tails at his device, as they would require GBytes of storage. To prevent the privacy leak, we propose to partition the credential IDs I into shards I1, I2, . . . , Is of limited size so that the tail set G(Ij) for each shard would be feasible to download and carry (say, a dozen of MBytes). Together with the credential to present, the User will then inform the Verifier of his shard number j so that the Verifier uses the corresponding accumulator data Aj . To make the shard index privacy-oblivious, we suggest attribute-based sharding, when Issuer selects q attributes {ai} of necessary granularity (year of birth, zip code, etc.) and partitions the credential set by each ai independently. For each attribute ai and each shard an accumulator Ai,j will be published. Nevertheless, since accumulators are of constant size, the total amount of space consumed by q× s accumulators is negligible. At the time of presentation the User selects the attribute on which he will prove the nonrevocation. This attribute should be among those that are already disclosed to the Verifier. Before presentation, User downloads the entire set Gi,j of tails that correspond to his share j at attribute i. As the tails are signed, he ask the Verifier to download, and this does not reduce the privacy. We also implement the revocati

We analyse the performance of several clustering algorithms in the digital peerto-peer currency Bitcoin. Clustering in Bitcoin refers to the task of finding addresses that belongs to the same wallet as a given address. In order to assess the effectiveness of clustering strategies we exploit a vulnerability in the implementation of Connection Bloom Filtering to capture ground truth data about 37,585 Bitcoin wallets and the addresses they own. In addition to well-known clustering techniques, we introduce two new strategies, apply them on addresses of the collected wallets and evaluate precision and recall using the ground truth. Due to the nature of the Connection Bloom Filtering vulnerability the data we collect is not without errors. We present a method to correct the performance metrics in the presence of such inaccuracies. Our results demonstrate that even modern wallet software can not protect its users properly. Even with the most basic clustering technique known as multiinput heuristic, an adversary can guess on average 68.59% addresses of a victim. We show that this metric can be further improved by combining several more sophisticated heuristics.

After more than six years from the launch of Bitcoin, it has become evident that the decentralized transaction ledger functionality implemented through the blockchain technology can be used not only for cryptocurrencies, but to register, confirm and transfer any kind of contract and property. In this work we analyze the most relevant functionalities and known issues of this technology, with the intent of pointing out the possible behaviours that are not as efficient as they should when thinking with a broader outlook. Our analysis would be the starting point for the introduction of a new approach to blockchain creation and management, which will be the subject of a forthcoming paper.

Android remains the dominant OS in the smartphone market even though the iOS share of the market increased during the iPhone 6 release period. As various types of Android smartphones are being launched in the market, forensic studies are being conducted to test data acquisition and analysis. However, since the application of new Android security technologies, it has become more difficult to acquire data using existing forensic methods. In order to address this problem, we propose a new acquisition method based on analyzing the firmware update protocols of Android smartphones. A physical acquisition of Android smartphones can be achieved using the flash memory read command by reverse engineering the firmware update protocol in the bootloader. Our experimental results demonstrate that the proposed method is superior to existing forensic methods in terms of the integrity guarantee, acquisition speed, and physical dump with screen-locked smartphones (USB debugging disabled). © 2015 The Authors. Published by Elsevier Ltd on behalf of DFRWS. This is an open access articleunder theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

In order to gain their customers' trust, software vendors can certify their products according to security standards, e.g., the Common Criteria (ISO 15408). However, a Common Criteria certification requires a comprehensible documentation of the software product. The creation of this documentation results in high costs in terms of time and money.
 We propose a software development process that supports the creation of the required documentation for a Common Criteria certification. Hence, we do not need to create the documentation after the software is built. Furthermore, we propose to use an enhanced version of the requirements-driven software engineering process called ADIT to discover possible problems with the establishment of Common Criteria documents. We aim to detect these issues before the certification process. Thus, we avoid expensive delays of the certification effort. ADIT provides a seamless development approach that allows consistency checks between different kinds of UML models. ADIT also supports traceability from security requirements to design documents. We illustrate our approach with the development of a smart metering gateway system.

Android platform has become the main target of the malware developers in the past few years. One of Android's main defense mechanisms against malicious apps is a permission-based access control mechanism. It is a feasible approach to detect a potential malicious application based on the permissions it requested. In this paper, we proposed a two-layered permission based detection scheme for detecting malicious Android applications. Comparing with previous researches, we consider the apps requested permission pairs as the additional condition, and we also consider used permissions to improve detection accuracy. The result of an evaluation, performed using 28548 benign apps and 1536 malicious apps, indicates that a two-layered permission-based detector has high detection rate of malware.

The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.

Personal health record (PHR) service is an emerging model for health information exchange. In PHR systems, patient’s health records and information are maintained by the patient himself through the Web. In reality, PHRs are often outsourced to be stored at the third parties like cloud service providers. However, there have been serious privacy concerns about cloud service as it may expose user’s sensitive data like PHRs to those cloud service providers or unauthorized users. Using attribute-based encryption (ABE) to encrypt patient’s PHRs in cloud environment, secure and flexible access control can be achieved. Yet, problems like scalability in key management, fine-grained access control, and efficient user revocation remain to be addressed. In this paper, we propose a privacy-preserving PHR, which supports fine-grained access control and efficient revocation. To be specific, our scheme achieves the goals (1) scalable and fine-grained access control for PHRs by using multi-authority ABE scheme, and (2) efficient on-demand user/attribute revocation and dynamic policy update. In our scheme, we consider the situation that multiple data owners exist, and patient’s PHRs are encrypted and stored in semi-trust servers. The access structure in our scheme is expressive access tree structure, and the security of our scheme can be reduced to the standard decisional bilinear Diffie–Hellman assumption.

The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.


Question: Optimizing Features and Detecting Root Exploits: A Blockchain and Mobile Device Approach for Managing Medical Data.

This paper explores advanced methods for detecting malware on Android devices, focusing on the unique challenges posed by the rapid proliferation of this operating system. With over 1.4 billion active users, Android's popularity has made it a prime target for cybercriminals seeking to exploit vulnerabilities and access sensitive user data. Despite efforts to develop robust detection mechanisms, existing solutions often fall short due to insufficient validation and practical applicability. Here, we propose two innovative approaches for identifying malicious applications: a network-based detection method and a system call-based detection technique. To assess the efficacy of these strategies, we conducted extensive testing on a sample set of 1260 malware samples sourced from the Android Malware Genome Project, alongside 227 benign applications. The results were promising, with the system call-based approach achieving an impressive detection accuracy of 87%. This finding is particularly noteworthy given the complexity and diversity of malware threats. Our research contributes to the field by offering security experts more accurate and quantifiable tools for combating Android malware, thereby enhancing the overall security landscape for mobile devices.

**Question**: A Comparative Study on Machine Learning Algorithms for Predicting Energy Consumption in Smart Grids

**Documents**: A new method for detecting and forecasting power demand in smart grids using machine learning
Smart grids, as the next generation of power supply networks, have been designed to integrate renewable energy sources and enhance the efficiency of electricity distribution. Accurate demand forecasting is crucial for the effective operation and planning of smart grids. This paper presents a novel method for detecting and forecasting power demand in smart grids using machine learning techniques. The proposed method involves the use of historical data on power consumption, weather conditions, and other relevant factors to train a machine learning model. The model is then used to predict future power demand, allowing for better management of electricity supply and demand. We compare the performance of several machine learning algorithms, including linear regression, support vector machines, and artificial neural networks, in terms of prediction accuracy and computational efficiency. The results show that artificial neural networks outperform other algorithms in terms of prediction accuracy, with an error rate of less than 5%. This study provides valuable insights into the application of machine learning for demand forecasting in smart grids, which can contribute to the optimization of energy resources and the reduction of carbon emissions.

**Similar Documents**: This study introduces a cutting-edge approach for demand detection and prediction in smart grids, leveraging the power of machine learning to optimize energy distribution and integration of renewable sources. Smart grids, designed to enhance the efficiency and reliability of electricity supply, require precise forecasting to

Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing.

Mobile health technologies offer great promise for reducing healthcare costs and improving patient care. Wearable and implantable technologies are contributing to a transformation in the mobile health era in terms of improving healthcare and health outcomes and providing real-time guidance on improved health management and tracking. In this article, we review the biomedical applications of wearable and implantable medical devices and sensors, ranging from monitoring to prevention of diseases, as well as the materials used in the fabrication of these devices and the standards for wireless medical devices and mobile applications. We conclude by discussing some of the technical challenges in wearable and implantable technology and possible solutions for overcoming these difficulties.

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.

Cloud computing offers great potential to improve productivity and reduce costs, but at the same time it possesses many new security risks. In this paper we identify the possible security attacks on clouds including: Wrapping attacks, Malware-Injection attacks, Flooding attacks, Browser attacks, and also Accountability checking problems. We identify the root causes of these attacks and propose specific

In the Big Data era, personal metadata may will become a new type of corporate asset, however there have already been a growing public concern about user’s privacy mined from metadata. In this paper we address the problem of implementing the self-sovereignty of personal metadata on the existing OpenPDS/SafeAnswers framework according to the Windhover Principle. In order to do that, we propose a new framework, called BlocakChain-based Personal Data Store (BCPDS), to realize two basic properties: notary and autonomy. This framework, firstly introduces the BlockChain as a notary, into OpenPDS/SafeAnswers for secure storage of personal meta-data instead of the original database. Next, we present an AutoNomybased Access Control (ANAC) to improve the SafeAnswers module, where ANAC is a new mechanism that enforces access based on the relationship among all authorized users and metadata’s owner. In addition, we also propose General Access Structure (GAS) and threshold secret sharing scheme in BlockChain as an implementation method for our BC-PDS framework.

In modern societies, the number of mobile users has dramatically risen in recent years. In this paper, an efficient authentication scheme for distributed mobile cloud computing services is proposed. The proposed scheme provides security and convenience for mobile users to access multiple mobile cloud computing services from multiple service providers using only a single private key. The security strength of the proposed scheme is based on bilinear pairing cryptosystem and dynamic nonce generation. In addition, the scheme supports mutual authentication, key exchange, user anonymity, and user untraceability. From system implementation point of view, verification tables are not required for the trusted smart card generator (SCG) service and cloud computing service providers when adopting the proposed scheme. In consequence, this scheme reduces the usage of memory spaces on these corresponding service providers. In one mobile user authentication session, only the targeted cloud service provider needs to interact with the service requestor (user). The trusted SCG serves as the secure key distributor for distributed cloud service providers and mobile clients. In the proposed scheme, the trusted SCG service is not involved in individual user authentication process. With this design, our scheme reduces authentication processing time required by communication and computation between cloud service providers and traditional trusted third party service. Formal security proof and performance analyses are conducted to show that the scheme is both secure and efficient.

Hyperledger Fabric is a "permissioned" blockchain architecture, providing a consistent distributed ledger, shared by a set of "peers." As with every blockchain architecture, the core principle of Hyperledger Fabric is that all the peers must have the same view of the shared ledger, making it challenging to support private data for the different peers. Extending Hyperledger Fabric to support private data (that can influence transactions) would open the door to many exciting new applications, in areas from healthcare to commerce, insurance, finance, and more. In this work we explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC). Specifically, in our solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction. This solution is very general, allowing in principle to base transactions on any combination of public and private data. We created a demo of our solution over Hyperledger Fabric v1.0, implementing a bidding system where sellers can list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price itself. We implemented a smart contract (aka "chaincode") that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. The chaincode itself was written in Go, and we used the SWIG library to make it possible to call our protocol implementation in C++. We identified two basic services that should be added to Hyperledger Fabric to support our solution, and are now working on implementing them.

Patient tracking is an important component toward creating smart cities. In this demo we use Bluetooth Low Energy beacons and single board computers to track patients in the emerging field of smart care facilities. Our model utilizes a fixed scanner moving transmitter method for wireless tracking of patients through the facility. The data collected by all scanners is stored within a central database that is designed to be efficiently queried. We show how inexpensive components in conjunction with free open source software can be used to implement a patient tracking system. We focus on the pipeline between acquisition and display of the location data. Additionally, we also discuss the manipulation of the data required for usability, and optional filtering operations that improve accuracy.

Android, the fastest growing mobile operating system released in November 2007, boasts of a staggering 1.4 billion active users. Android users are susceptible to malicious applications that can hack into their personal data due to the lack of careful monitoring of their in-device security. There have been numerous works on devising malware detection methods. However, none of earlier works are conclusive enough for direct application and lack experimental validation. In this paper, we have investigated the natures and identities of malicious applications and devised two novel detection approaches for detection: network-based detection and system call based detection approaches. To evaluate our proposed approaches, we performed experiments on a subset of 1260 malwares, acquired from Android Malware Genome Project, a malware database created by Y. Zhou et al. [1] and 227 non-malware (benign) applications. Results show that our system call based approach is able to detect malwares with an accuracy of 87% which is quite significant in general malware detection context. Our proposed detection approaches along with the experimental results will provide security professionals with more precise and quantitative approaches in their investigations of mobile malwares on Android systems.

Besides attracting a billion dollar economy, Bitcoin revolutionized the field of digital currencies and influenced many adjacent areas. This also induced significant scientific interest. In this survey, we unroll and structure the manyfold results and research directions. We start by introducing the Bitcoin protocol and its building blocks. From there we continue to explore the design space by discussing existing contributions and results. In the process, we deduce the fundamental structures and insights at the core of the Bitcoin protocol and its applications. As we show and discuss, many key ideas are likewise applicable in various other fields, so that their impact reaches far beyond Bitcoin itself.


Question: Optimizing Features and Detecting Root Exploits: A Blockchain and Mobile Device Approach for Managing Medical Data.

This paper introduces an advanced method for detecting Android malware with high accuracy by leveraging ensemble learning techniques. Given the staggering popularity of Android devices, with over 50 billion app downloads and more than 1.3 million applications available in Google's official marketplace, the platform has become a prime target for malware developers. Recent malware variants have become increasingly sophisticated, employing intricate methods to evade detection. As traditional signature-based approaches struggle to keep up with zero-day threats, there is a pressing need for innovative solutions. To address this challenge, the paper proposes an ensemble learning-based approach for Android malware detection. This method combines the strengths of static analysis with the efficiency and robustness of ensemble machine learning algorithms, leading to enhanced accuracy in identifying malware. The models are trained using a vast dataset of malware samples and benign applications sourced from a leading antivirus provider. The results of the experiments reveal that the proposed technique, which exploits a comprehensive feature set to harness the power of ensemble learning, achieves a detection accuracy of 97.3% to 99% with minimal false positives. Keywords: mobile security, Android, malware detection, ensemble learning, static analysis, machine learning, data mining, random forest.

**Question**: Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management

**Documents**: Blockchain-based Medical Data Sharing: Security, Privacy, and Performance
The rapid growth of digital health technologies has led to an exponential increase in the volume of medical data generated by patients. This data is critical for improving healthcare outcomes, but its management poses significant challenges, particularly in terms of security, privacy, and interoperability. Blockchain technology offers a promising solution to these issues by providing a decentralized, secure, and transparent platform for medical data sharing. This paper discusses the application of blockchain in the context of medical data management, focusing on its potential to enhance security, protect patient privacy, and improve the performance of healthcare systems. The paper also examines the technical challenges associated with implementing blockchain in healthcare and proposes strategies for overcoming these obstacles. Through a detailed analysis of existing blockchain-based medical data sharing systems, the paper highlights the benefits and limitations of this technology, providing insights for future research and development in the field. Keywords: blockchain, medical data, security, privacy, performance, healthcare systems, interoperability, digital health technologies.

**Similar Documents**: Blockchain for Medical Data Management: Enhancing Security, Privacy, and Performance in Healthcare
The proliferation of digital health technologies has resulted in a surge of medical data, which is essential for advancing healthcare but presents significant management challenges,

A log is a collection of record of events that occurs within an organization containing systems and network. These logs are very important for any organization, because log file will able to record all user activities. As this log files plays vital role and also it contains sensitive information , it should be maintained highly secure. So, management and securely maintenance of log records are very tedious task. However, deploying such a system for high security and privacy of log records is an overhead for an organization and also it requires additional cost. Many techniques have been design so far for security of log records. The alternative solution is to maintaining log records over a cloud database. Log files over cloud environment leads to challenges about privacy, confidentiality and integrity of log files. In this paper, we propose highly secured cloud rendered log management and also use of some cryptographic algorithms for dealing the issues to access a cloud based data storage. To the best of knowledge, this is the strong work to provide a complete solution to the cloud based secure log management problem.

This report describes an analysis of the Fitbit Flex ecosystem. Our objectives are to describe (1) the data Fitbit collects from its users, (2) the data Fitbit provides to its users, and (3) methods of recovering data not made available to device owners. Our analysis covers four distinct attack vectors. First, we analyze the security and privacy properties of the Fitbit device itself. Next, we observe the Bluetooth traffic sent between the Fitbit device and a smartphone or personal computer during synchronization. Third, we analyze the security of the Fitbit Android app. Finally, we study the security properties of the network traffic between the Fitbit smartphone or computer application and the Fitbit web service. We provide evidence that Fitbit unnecessarily obtains information about nearby Flex devices under certain circumstances. We further show that Fitbit does not provide device owners with all of the data collected. In fact, we find evidence of per-minute activity data that is sent to the Fitbit web service but not provided to the owner. We also discovered that MAC addresses on Fitbit devices are never changed, enabling usercorrelation attacks. BTLE credentials are also exposed on the network during device pairing over TLS, which might be intercepted by MITM attacks. Finally, we demonstrate that actual user activity data is authenticated and not provided in plaintext on an end-to-end basis from the device to the Fitbit web service.

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model [1] by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.

Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement. Social implications – Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk. Originality/value – The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, in this paper we develop and analyze proactive Machine Learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification based solutions for detecting unknown Android malware.

Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.

The blockchain initially gained traction in 2008 as the technology underlying Bitcoin [105], but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work (PoW), (ii) proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.

Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data. In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning.

A new development of smart-home systems is to use mobile apps to control IoT devices across a Home Area Network (HAN). As verified in our study, those systems tend to rely on the Wi-Fi router to authenticate other devices. This treatment exposes them to the attack from malicious apps, particularly those running on authorized phones, which the router does not have information to control. Mitigating this threat cannot solely rely on IoT manufacturers, which may need to change the hardware on the devices to support encryption, increasing the cost of the device, or software developers who we need to trust to implement security correctly. In this work, we present a new technique to control the communication between the IoT devices and their apps in a unified, backward-compatible way. Our approach, called HanGuard, does not require any changes to the IoT devices themselves, the IoT apps or the OS of the participating phones. HanGuard uses an SDN-like approach to offer fine-grained protection: each phone runs a non-system userspace Monitor app to identify the party that attempts to access the protected IoT device and inform the router through a control plane of its access decision; the router enforces the decision on the data plane after verifying whether the phone should be allowed to talk to the device. We implemented our design over both Android and iOS (> 95% of mobile OS market share) and a popular router. Our study shows that HanGuard is both efficient and effective in practice.


Question: Root exploit detection and optimization of features: a mobile device and blockchain-based system for managing medical data.

This paper presents Crowdroid, a novel behavior-based malware detection system specifically designed for the Android platform. Given the exponential growth in the adoption of smartphones, particularly those running on Android, which is poised to dominate the market, the demand for robust malware analysis tools has become imperative. Building upon previous methodologies for dynamic analysis of application behavior, Crowdroid serves as a crucial component within a comprehensive framework that harnesses the power of crowdsourcing to gather trace data from a vast network of real users. This data is then analyzed on a centralized server, using both synthetic malware created for testing purposes and real-world malware samples. The effectiveness of Crowdroid in identifying and isolating malware, thereby alerting users to potential threats, is demonstrated through these analyses. This capability is crucial in preventing the propagation of detected malware to a broader audience, showcasing the system's potential to significantly mitigate the spread of malicious software on the Android platform.

Mobile devices have become ubiquitous due to centralization of private user information, contacts, messages and multiple sensors. Google Android, an open-source mobile Operating System (OS), is currently the market leader. Android popularity has motivated the malware authors to employ set of cyber attacks leveraging code obfuscation techniques. Obfuscation is an action that modifies an application (app) code, preserving the original semantics and functionality to evade anti-malware. Code obfuscation is a contentious issue. Theoretical code analysis techniques indicate that, attaining a verifiable and secure obfuscation is impossible. However, obfuscation tools and techniques are popular both among malware developers (to evade anti-malware) and commercial software developers (protect intellectual rights). We conducted a survey to uncover answers to concrete and relevant questions concerning Android code obfuscation and protection techniques. The purpose of this paper is to review code obfuscation and code protection practices, and evaluate efficacy of existing code de-obfuscation tools. In particular, we discuss Android code obfuscation methods, custom app protection techniques, and various de-obfuscation methods. Furthermore, we review and analyze the obfuscation techniques used by malware authors to evade analysis efforts. We believe that, there is a need to investigate efficiency of the defense techniques used for code protection. This survey would be beneficial to the researchers and practitioners, to understand obfuscation and de-obfuscation techniques to propose novel solutions on Android.

The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.

Malware is code designed for a malicious purpose, such as obtaining root privilege on a host. A malware detector identifies malware and thus prevents it from adversely affecting a host. In order to evade detection, malware writers use various obfuscation techniques to transform their malware. There is strong evidence that commercial malware detectors are susceptible to these evasion tactics. In this paper, we describe the design and implementation of a malware transformer that reverses the obfuscations performed by a malware writer. Our experimental evaluation demonstrates that this malware transformer can drastically improve the detection rates of commercial malware detectors.

Sensitive data such as text messages, contact lists, and personal information are stored on mobile devices. This makes authentication of paramount importance. More security is needed on mobile devices since, after point-of-entry authentication, the user can perform almost all tasks without having to re-authenticate. For this reason, many authentication methods have been suggested to improve the security of mobile devices in a transparent and continuous manner, providing a basis for convenient and secure user re-authentication. This paper presents a comprehensive analysis and literature review on transparent authentication systems for mobile device security. This review indicates a need to investigate when to authenticate the mobile user by focusing on the sensitivity level of the application, and understanding whether a certain application may require a protection or not.

Blockchain protocols such as Bitcoin are gaining traction for exchanging payments in a secure and decentralized manner. Their need to achieve consensus across a large number of participants, however, fundamentally limits their performance. We describe Teechain, a new off-chain payment protocol that utilizes trusted execution environments (TEEs) to perform secure, efficient and scalable fund transfers on top of a blockchain, with asynchronous blockchain access. Teechain introduces secure payment chains to route payments across multiple payment channels. Teechain mitigates failures of TEEs with two strategies: (i) backups to persistent storage and (ii) a novel variant of chain-replication. We evaluate an implementation of Teechain using Intel SGX as the TEE and the operational Bitcoin blockchain. Our prototype achieves orders of magnitude improvement in most metrics compared to existing implementations of payment channels: with replicated Teechain nodes in a trans-atlantic deployment, we measure a throughput of over 33, 000 transactions per second with 0.1 second latency.

The increasing number of repeated malware penetrations into official mobile app markets poses a high security threat to the confidentiality and privacy of end users’ personal and sensitive information. Protecting end user devices from falling victims to adversarial apps presents a technical and research challenge for security researchers/engineers in academia and industry. Despite the security practices and analysis checks deployed at app markets, malware sneak through the defenses and infect user devices. The evolution of malware has seen it become sophisticated and dynamically changing software usually disguised as legitimate apps. Use of highly advanced evasive techniques, such as encrypted code, obfuscation and dynamic code updates, etc., are common practices found in novel malware. With evasive usage of dynamic code updates, a malware pretending as benign app bypasses analysis checks and reveals its malicious functionality only when installed on a user’s device. This dissertation provides a thorough study on the use and the usage manner of dynamic code updates in Android apps. Moreover, we propose a hybrid analysis approach, StaDART, that interleaves static and dynamic analysis to cover the inherent shortcomings of static analysis techniques to analyze apps in the presence of dynamic code updates. Our evaluation results on real world apps demonstrate the effectiveness of StaDART. However, typically dynamic analysis, and hybrid analysis too for that matter, brings the problem of stimulating the app’s behavior which is a non-trivial challenge for automated analysis tools. To this end, we propose a backward slicing based targeted inter component code paths execution technique, TeICC. TeICC leverages a backward slicing mechanism to extract code paths starting from a target point in the app. It makes use of a system dependency graph to extract code paths that involve inter component communication. The extracted code paths are then instrumented and executed inside the app context to capture sensitive dynamic behavior, resolve dynamic code updates and obfuscation. Our evaluation of TeICC shows that it can be effectively used for targeted execution of inter component code paths in obfuscated Android apps. Also, still not ruling out the possibility of adversaries reaching the user devices, we propose an on-phone API hooking

Patient tracking is an important component toward creating smart cities. In this demo we use Bluetooth Low Energy beacons and single board computers to track patients in the emerging field of smart care facilities. Our model utilizes a fixed scanner moving transmitter method for wireless tracking of patients through the facility. The data collected by all scanners is stored within a central database that is designed to be efficiently queried. We show how inexpensive components in conjunction with free open source software can be used to implement a patient tracking system. We focus on the pipeline between acquisition and display of the location data. Additionally, we also discuss the manipulation of the data required for usability, and optional filtering operations that improve accuracy.

This research proposed a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant.

A log is a collection of record of events that occurs within an organization containing systems and network. These logs are very important for any organization, because log file will able to record all user activities. As this log files plays vital role and also it contains sensitive information , it should be maintained highly secure. So, management and securely maintenance of log records are very tedious task. However, deploying such a system for high security and privacy of log records is an overhead for an organization and also it requires additional cost. Many techniques have been design so far for security of log records. The alternative solution is to maintaining log records over a cloud database. Log files over cloud environment leads to challenges about privacy, confidentiality and integrity of log files. In this paper, we propose highly secured cloud rendered log management and also use of some cryptographic algorithms for dealing the issues to access a cloud based data storage. To the best of knowledge, this is the strong work to provide a complete solution to the cloud based secure log management problem.

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.


Question: Detection of root exploits and optimization of features: a mobile device and blockchain-based approach to managing medical data.

This paper delves into the landscape of Android malware, focusing on its characterization and evolution, to address the escalating threat posed by mobile malware on popular platforms like Android. Given the swift proliferation of smart devices, the emergence of mobile malware has become a pressing concern, necessitating robust defense mechanisms. However, our ability to counteract these threats is hindered by a lack of comprehensive understanding and timely access to malware samples. Targeting the Android ecosystem, we have embarked on an extensive effort to gather over 1,200 malware samples, encompassing a wide spectrum of Android malware families from their inception in August 2010 to those active in October 2011. Our analysis encompasses various facets, such as installation methods, activation mechanisms, and the nature of malicious payloads. Through this systematic characterization, we observe that malware is rapidly evolving to evade detection by conventional mobile antivirus software. Our experimental evaluation, involving four prominent mobile security applications, reveals that detection rates range from a high of 79.6% to a low of 20.2% within our dataset. These findings underscore the urgent need for the development of advanced anti-mobile-malware solutions that can effectively counteract the sophisticated tactics employed by contemporary malware.

**Question**: A Novel Method for Enhancing the Security of Blockchain Transactions

**Documents**: A Lightweight Blockchain for Mobile Devices
Blockchain technology has gained significant attention due to its decentralized, secure, and transparent nature. However, its application on mobile devices faces challenges due to the resource constraints of these devices. This paper presents a lightweight blockchain framework designed specifically for mobile devices. The proposed framework, named MobileChain, aims to reduce the computational and storage overheads while maintaining the security and integrity of the blockchain. MobileChain utilizes a novel consensus algorithm that is optimized for the limited resources of mobile devices. It also employs a sharding technique to partition the blockchain into smaller segments, thereby reducing the storage requirements. The experimental results demonstrate that MobileChain can achieve higher transaction throughput and lower latency compared to existing blockchain systems while consuming less energy and storage space. This makes it a promising solution for implementing blockchain on mobile devices.

**Similar Documents**: In light of the burgeoning interest in blockchain technology, attributed to its decentralized, secure, and transparent attributes, the adaptation of this technology for mobile devices has emerged as a critical area of focus. The inherent resource limitations of mobile devices, however, pose significant hurdles in the direct application of blockchain systems. This paper introduces a tailored blockchain framework, MobileChain, engineered to address the computational and storage constraints

Healthcare professionals spend much of their time wandering between patients and offices, while the supportive technology stays stationary. Therefore, mobile applications has adapted for healthcare industry. In spite of the advancement and variety of available mobile based applications, there is an eminent need to investigate the current position of the acceptance of those mobile health applications that are tailored towards the tracking patients condition, share patients information and access. Consequently, in this study Technology Acceptance Model has designed to investigate the user acceptance of mobile technology application within healthcare industry. The purpose of this study is to design a quantitative approach based on the technology acceptance model questionnaire as its primary research methodology. It utilized a quantitative approach based a Technology Acceptance Model (TAM) to evaluate the system mobile tracking Model. The related constructs for evaluation are: Perceived of Usefulness, Perceived Ease of Use, User Satisfaction and Attribute of Usability. All these constructs are modified to suit the context of the study. Moreover, this study outlines the details of each construct and its relevance toward the research issue. The outcome of the study represents series of approaches that will apply for checking the suitability of a mobile tracking on patient progress application for health care industry and how well it achieves the aims and objectives of the design.

The recent proliferation of the Internet of Things (IoT) technology poses major security and privacy concerns. Specifically, the use of personal IoT devices, such as tablets, smartphones, and even smartwatches, as part of the Bring Your Own Device (BYOD) trend, may result in severe network security breaches in enterprise environments. Such devices increase the attack surface by weakening the digital perimeter of the enterprise network and opening new points of entry for malicious activities. In this paper we demonstrate a novel attack scenario in an enterprise environment by exploiting the smartwatch device of an innocent employee. Using a malicious application running on a suitable smartwatch, the device imitates a real Wi-Fi direct printer service in the network. Using this attack scenario, we illustrate how an advanced attacker located outside of the organization can leak/steal sensitive information from the organization by utilizing the compromised smartwatch as a means of attack. An attack mitigation process and countermeasures are suggested in order to limit the capability of the remote attacker to execute the attack on the network, thus minimizing the data leakage by the smartwatch.

Android, being an open source smartphone operating system, enjoys a large community of developers who create new mobile services and applications. However, it also attracts malware writers to exploit Android devices in order to distribute malicious apps in the wild. In fact, Android malware are becoming more sophisticated and they use advanced “dynamic loading” techniques like Java reflection or native code execution to bypass security detection. To detect dynamic loading, one has to use dynamic analysis. Currently, there are only a handful of Android dynamic analysis tools available, and they all have shortcomings in detecting dynamic loading. The aim of this paper is to design and implement a dynamic analysis system which allows analysts to perform systematic analysis of dynamic payloads with malicious behaviors. We propose “DroidTrace”, a ptrace based dynamic analysis system with forward execution capability. Our system uses ptrace to monitor selected system calls of the target process which is running the dynamic payloads, and classifies the payloads behaviors through the system call sequence, e.g., behaviors such as file access, network connection, inter-process communication and even privilege escalation. Also, DroidTrace performs “physical modification” to trigger different dynamic loading behaviors within an app. Using DroidTrace, we carry out a large scale analysis on 36,170 dynamic payloads in 50,000 apps and 294 malware in 10 families (four of them are zero-day) with various dynamic loading behaviors.

Recent years have witnessed the increasing threat of phishing attacks on mobile computing platforms. In fact, mobile phishing is particularly dangerous due to the hardware limitations of mobile devices and the habits of mobile users. In this paper, we did a comprehensive study on the security vulnerabilities caused by mobile phishing attacks, including web page phishing attacks, application phishing attacks, and account registry phishing attacks. Existing schemes designed for web phishing attacks on personal computers (PCs) cannot effectively address the various phishing attacks on mobile devices. Hence, we propose MobiFish, which is a novel automated lightweight antiphishing scheme for mobile platforms. MobiFish verifies the validity of web pages, applications, and persistent accounts by comparing the actual identity to the claimed identity. MobiFish has been implemented on a Nexus 4 smartphone running the Android 4.2 operating system. We experimentally evaluate the performance of MobiFish with 100 phishing URLs and corresponding legitimate URLs, as well as phishing apps. The results show that MobiFish is very effective in detecting phishing attacks on mobile phones.

Mobile healthcare is a fast growing area of research that capitalizes on mobile technologies and wearables to provide realtime and continuous monitoring and analysis of vital signs of users. Yet, most of the current applications are developed for general population without taking into consideration the context and needs of different user groups. Designing and developing mobile health applications and diaries according to the user context can significantly improve the quality of user interaction and encourage the application use. In this paper, we propose a user context model and a set of usability attributes for developing mobile applications in healthcare. The proposed model and the selected attributes are integrated into a mobile application development framework to provide user-centered and context-aware guidelines. To validate our framework, a mobile diary was implemented for patients undergoing Peritoneal Dialysis (PD) and tested with real users.

With the roaring growth and wide adoption of smart mobile devices, users are continuously integrating with culture of the mobile applications (apps). These apps are not only gaining access to information on the smartphone but they are also able gain users' authorization to access remote servers on their behalf. The Open standard for Authorization (OAuth) is widely used in mobile apps for gaining access to user's resources on remote service providers. In this paper, we analyze the different OAuth implementations adopted by the SDKs of the popular resource providers on smartphones and demonstrate possible attacks on most OAuth implementations. By analyzing source code of more than 430 popular Android apps we summarized the trends followed by the service providers and by the OAuth development choices made by application developers. In addition, we propose an application-based OAuth Manager framework, that provides a secure OAuth flow in smartphones that is based on the concept of privilege separation and does not require high overhead.

Healthcare is one of the most rapidly expanding application areas of the Internet of Things (IoT) technology. IoT devices can be used to enable remote health monitoring of patients with chronic diseases such as cardiovascular diseases (CVD). In this paper we develop an algorithm for ECG analysis and classification for heartbeat diagnosis, and implement it on an IoT-based embedded platform. This algorithm is our proposal for a wearable ECG diagnosis device, suitable for 24-hour continuous monitoring of the patient. We use Discrete Wavelet Transform (DWT) for the ECG analysis, and a Support Vector Machine (SVM) classifier. The best classification accuracy achieved is 98.9%, for a feature vector of size 18, and 2493 support vectors. Different implementations of the algorithm on the Galileo board, help demonstrate that the computational cost is such, that the ECG analysis and classification can be performed in real-time.

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

We consider the problem of patient self-controlled access privilege to highly sensitive Personal Health Information (PHI), where PHI is expected to be securely stored in cloud storage for uninterrupted anytime, anywhere remote access. In order to assure the privacy of PHI, we propose Efficient and Secure Patient-centric Access Control (ESPAC) scheme which allows data requesters to have different access privileges based on their roles, and then assigns different attribute sets to them. Extensive security and performance analyses demonstrate that the ESPAC scheme is able to achieve desired security requirements with acceptable communication delay.

For the Android malware detection / classification anti-malware community has relied on traditional malware detection methods as a countermeasure. However, traditional detection methods are developed for detecting the computer malware, which is different from Android malware in structure and characteristics. Thus, they may not be useful for Android malware detection. Moreover, majority of suggested detection approaches may not be generalized and are incapable of detecting zero-day malware due to different reasons such as available data set with specific set of examples. Thus, their detection accuracy may be questionable. To address this problem, this paper presents a malware classification approach with a reliable detection accuracy and evaluate the approach using artificially generated examples. The suggested approach generates the signature profiles and behavior profiles of each application in the data set, which are further used as input for the classification task. For improving the detection accuracy, feature fusion of features from filter methods and wrapper method and algorithm fusion is investigated. Without affecting the detection accuracy, the optimal balance between real world examples and synthetic examples is also investigated. The experimental results suggest that both AUC and F1 can be obtained up to 0.94 for both known and unknown malware using original examples and synthetic examples.


Question: Swarm: Enhanced awareness, precise coordination, and intelligent convergence via mobile group text messaging.

This paper explores the outcomes of an ethnographic investigation into the usage of instant messaging (IM) in professional settings, shedding light on its significance for media theory. The research reveals that IM facilitates a wide range of informal communication activities, enhancing the flexibility and expressiveness of interactions. The study uncovers novel applications of IM, underscoring communication elements often overlooked by contemporary media theory. These elements pertain to the strategies individuals employ to establish connections and govern communication, rather than merely focusing on the exchange of information. The authors term these strategies "outeraction". The paper delves into how outeraction influences media selection and the dynamics of media utilization, illustrating the multifaceted role of IM in shaping workplace communication patterns.

**Question**: A Novel Method for Detecting and Mitigating DDoS Attacks in IoT Networks

**Documents**: A hybrid approach to detect and mitigate DDoS attacks in SDN networks
In this paper, we propose a hybrid approach to detect and mitigate DDoS attacks in SDN (Software Defined Networking) networks. Our proposed system consists of two parts: a detection phase and a mitigation phase. In the detection phase, we use a machine learning approach based on a random forest algorithm to classify network traffic as normal or malicious. In the mitigation phase, we use a combination of rate limiting and blacklisting techniques to mitigate the effects of DDoS attacks. Our proposed system is designed to be integrated into the SDN controller, which allows it to make fast and intelligent decisions about how to handle network traffic. We evaluate our proposed system using a dataset of real-world DDoS attacks and compare its performance to existing approaches. Our results show that our proposed system can detect and mitigate DDoS attacks with high accuracy and low false positive rates.

**Similar Documents**: This work introduces a comprehensive strategy for identifying and countering DDoS attacks in SDN (Software Defined Networking) networks, employing a dual-phase mechanism. The initial phase involves detection, utilizing a machine learning methodology grounded in the random forest algorithm to categorize network traffic as benign or malevolent. The subsequent phase is focused on mitigation, combining rate limiting with blacklisting techniques to effectively address the repercussions of DDoS attacks. The proposed system is designed to be seamlessly integrated with the SDN controller, enabling swift and intelligent decision-making regarding network traffic management. The efficacy of this approach is assessed using a dataset comprising authentic DDoS attack instances, with the outcomes demonstrating superior performance and reduced false positives compared to conventional methods.

This practitioner paper outlines some of the benefits for the use of social media, from the perspective of a locallevel or county-level emergency manager (EM). As compared to state and national level emergency management, because local level EMs have limited manpower and resources, social media can positively or negatively impact the effectiveness of communication before, during and after disaster strikes. Outlined in this paper are six key points where local EMs have specific needs that could be addressed by the effective use of social media and, in the opinion of the authors, represent the top issues that EMs face when considering how to leverage Twitter, Facebook, YouTube, Instagram and other social media platforms. The six needs addressed in this paper include: 1) Best practices for general social media use by EMs, 2) Social media use for internal command and control within the EM group, 3) Developing situation awareness by monitoring social media, especially prior to predicable events, 4) Communicating disaster preparedness messages through social media, 5) Using social media for gathering damage assessment information during, or immediately following a crisis, and 6) Leveraging social media volunteer groups. This short paper picks up where the Federal Emergency Management Agency’s social media training leaves off and attempts to represent these six needs as use cases for researchers and developers to address in future publications and products.

With the advancement of mobile technology the use of Smartphone is greatly increased. Everyone has the mobile phones and it becomes the necessity of life. Today, smart devices are flooding the internet data at every time and in any form that cause the mobile crowdsensing (MCS). One of the key challenges in mobile crowd sensing system is how to effectively identify and select the well-suited participants in recruitments from a large user pool. This research work presents the concept of crowdsensing along with the selection process of participants from a large user pool. MCS provides the efficient selection process for participants that how well suited participant’s selects/recruit from a large user pool. For this, the proposed selection algorithm plays our role in which the recruitment of participants takes place with the availability status from the large user pool. At the end, the graphical result presented with the suitable location of the participants and their time slot. Keywords—Mobile crowdsensing (MCS); Mobile Sensing Platform (MSP]); crowd sensing; participant; user pool; crowdsourcing

We present a beacon-based clustering algorithm aimed at prolonging the cluster lifetime in VANETs. We use a new aggregate local mobility criterion to decide upon cluster re-organisation. The scheme incorporates a contention method to avoid triggering frequent re-organisations when two clusterheads encounter each other for a short period of time. Simulation results show a significant improvement of cluster lifetime and reduced node state/role changes compared to previous popular clustering algorithms.

Intelligent conversational assistants, such as Apple’s Siri, Microsoft’s Cortana, and Amazon’s Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly converses with end users over time by recruiting workers on demand, which in turn decide what might be the best response for each user sentence. Up to the first month of our deployment, 59 users have held conversations with Chorus during 320 conversational sessions. In this paper, we present an account of Chorus’ deployment, with a focus on four challenges: (i) identifying when conversations are over, (ii) malicious users and workers, (iii) on-demand recruiting, and (iv) settings in which consensus is not enough. Our observations could assist the deployment of crowd-powered conversation systems and crowd-powered systems in general.

We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation. We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another. The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area. From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with PLANNER, CONNIVER, HEARSAY-n, and PUP6 are used to demonstrate that negotiation--the two-way transfer of information--is a natural extension to the transfer of control mechanisms used in earlier problem-

BACKGROUND
Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder.

OBJECTIVE
The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing.

METHODS
We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients' mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients' self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks.

RESULTS
Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (beta(week) = -.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta(week) = -.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (b(week) = -.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (beta(week) = -.71, P < .001, per-protocol Cohen d = 2.58).

CONCLUSIONS
Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed.

TRIAL REGISTRATION
Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n).

This paper presents an approach to using a large team of UAVs to find radio frequency (RF) emitting targets in a large area. Small, inexpensive UAVs that can collectively and rapidly determine the approximate location of intermittently broadcasting and mobile RF emitters have a range of applications in both military, e.g., for finding SAM batteries, and civilian, e.g., for finding lost hikers, domains. Received Signal Strength Indicator (RSSI) sensors on board the UAVs measure the strength of RF signals across a range of frequencies. The signals, although noisy and ambiguous due to structural noise, e.g., multipath effects, overlapping signals and sensor noise, allow estimates to be made of emitter locations. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. Once likely target locations are identified, EO-camera equipped UAVs must be tasked to provide a video stream of the area to allow a user to identify the emitter.

Open-source software, usually created by volunteer programmers dispersed worldwide, now competes with that developed by software firms. This achievement is particularly impressive as open-source programmers rarely meet. They rely heavily on electronic media, which preclude the benefits of face-to-face contact that programmers enjoy within firms. In this paper, we describe findings that address this paradox based on observation, interviews and quantitative analyses of two open-source projects. The findings suggest that spontaneous work coordinated afterward is effective, rational organizational culture helps achieve agreement among members and communications media moderately support spontaneous work. These findings can imply a new model of dispersed collaboration.

Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. In the same time, flames (such as rants, taunts, and squalid phrases) are offensive/abusive phrases which might attack or offend the users for a variety of reasons. An automatic discriminative software with a sensitivity parameter for flame or abusive language detection would be a useful tool. Although a human could recognize these sorts of useless annoying texts among the useful ones, it is not an easy task for computer programs. In this paper, we describe an automatic flame detection method which extracts features at different conceptual levels and applies multilevel classification for flame detection. While the system is taking advantage of a variety of statistical models and rule-based patterns, there is an auxiliary weighted pattern repository which improves accuracy by matching the text to its graded entries.

As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for reallife applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the wellknown filter-refinement framework. In the filter step, we apply linesimplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper’s proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.