Question: Applying MPTCP subflow association control for the optimization of heterogeneous wireless networks.

This paper delves into the optimization of user association for load balancing in heterogeneous cellular networks, where the integration of small cell technology is pivotal for enhancing the capacity of macrocell-based networks. To effectively leverage small cell tiers, such as pico and femtocells, mobile users must be strategically directed towards these less congested networks, even at the expense of lower instantaneous signal-to-interference-plus-noise ratio (SINR) compared to the macrocell base station (BS). The optimization of long-term data rates for each user typically necessitates solving a complex utility maximization problem involving all SINRs and BS loads. However, practical implementations often employ a simplified biasing strategy, where a BS in a specific tier j appears more attractive by scaling its SINR with a factor Aj ≥ 1, effectively countering the heavy load on the macrocell. This paper aims to bridge the gap between these theoretical and practical approaches by proposing physical relaxations of the network-wide association problem, which is known to be NP-hard. We present a low-complexity, distributed algorithm that converges to a solution that is nearly optimal, with a guaranteed theoretical performance, and we demonstrate that simple biasing per tier can achieve comparable results when the bias values Aj are selected judiciously. Our numerical findings highlight a significant throughput improvement of 3.5 times for users at the cell edge and a 2 times rate gain for median users, compared to an association strategy that prioritizes received power maximization.

Wireless sensor networks (WSNs) are being deployed for an ever-widening range of applications, particularly due to their low power consumption and reliability. In most applications, the sensor data must be sent over the Internet or core network, which results in the WSN backhaul problem. Passive optical networks (PONs), a next-generation access network technology, are well suited form a backhaul which interfaces WSNs to the core network. In this paper a new structure is proposed which converges WSNs and PONs. It is modelled and analyzed through queuing theory by employing two M/M/1 queues in tandem. Numerical results show how the WSN and PON dimensions affect the average queue length, thus serving as a guideline for future resource allocation and scheduling of such a converged network.

RouteFlow is a platform for providing virtual IP routing services in OpenFlow networks. During the first year of development, we came across some use cases that might be interesting pursuing in addition to a number of lessons learned worth sharing. In this paper, we will discuss identified requirements and architectural and implementation changes made to shape RouteFlow into a more robust solution for Software Defined networking (SDN). This paper addresses topics of interest to the SDN community, such as development issues involving layered applications on top of network controllers, ease of configuration, and network visualization. In addition, we will present the first publicly known use case with multiple, heterogeneous OpenFlow controllers to implement a centralized routing control function, demonstrating how IP routing as a service can be provided for different network domains under a single central control. Finally, performance comparisons and a real testbed were used as means of validating the implementation.

Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is becoming the de-facto data format and streaming solution for over-the-top (OTT) video on-demand streaming. The underlying HTTP transport has its limitations and known penalties in slow start, under-utilization, and other inefficiency. An alternate approach to DASH is the new transport schemes like HTTP/2.0 and WebSocket. In this work, we explore WebRTC as a streaming transport to carry DASH data and demonstrate that it is competitive in serving low delay streaming applications with fast channel switching, with proper video data coding and streaming signaling/control solutions.

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.

Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.

1Department of Mobile Communications, School of Electrical Engineering and Computer Sciences, Technical University of Berlin, Berlin, Germany 2Wireless Networking, Signal Processing and Security Lab, Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA 3Division of Communication Systems, Department of Electrical Engineering (ISY), Linköping University, SE-581 83 Linköping, Sweden 4Communications Laboratory, Faculty of Electrical Engineering and Information Technology, Dresden University of Technology, 01062 Dresden, Germany

The purposes of this paper have to discuss issues related to Network Traffic Management. A relatively new category of network management is fast becoming a necessity in converged business Networks. Mid-sized and large organizations are finding they must control network traffic behavior to assure that their strategic applications always get the resources they need to perform optimally. Controlling network traffic requires limiting bandwidth to certain applications, guaranteeing minimum bandwidth to others, and marking traffic with high or low priorities. This exercise is called Network Traffic Management.

Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.

We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints connectivity and biconnectivity, and one optimization objective maximum power used. We present two centralized algorithms for use in static networks, and prove their optimality. For mobile networks, we present two distributed heuristics that adaptively adjust node transmit powers in response to topological changes and attempt to maintain a connected topology using minimum power. We analyze the throughput, delay, and power consumption of our algorithms using a prototype software implementation, an emulation of a power-controllable radio, and a detailed channel model. Our results show that the performance of multihop wireless networks in practice can be substantially increased with topology control.

Wireless power transfer is a promising technology to fundamentally address energy problems in a wireless sensor network. To make such a technology work effectively, a vehicle is needed to carry a charger to travel inside the network. On the other hand, it has been well recognized that a mobile base station offers significant advantages over a fixed one. In this paper, we investigate an interesting problem of co-locating the mobile base station on the wireless charging vehicle. We study an optimization problem that jointly optimizes traveling path, stopping points, charging schedule, and flow routing. Our study is carried out in two steps. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem.


Question: Utilizing MPTCP subflow association control for the optimization of heterogeneous wireless networks.

This paper presents a pragmatic traffic management framework, dubbed ATOM, tailored for integrated LTE-WiFi networks, aimed at addressing the escalating bandwidth demands of modern applications. Mobile network operators are increasingly turning to WiFi as a relief valve for their congested networks, yet they often struggle with the absence of advanced mechanisms to govern user access to WiFi. This deficiency leads to suboptimal network utilization, compromising the overall quality of experience (QoE) for users. Recognizing the urgent need for solutions that can equitably distribute user traffic between cellular and WiFi networks, we have engineered ATOM, an end-to-end adaptive traffic offloading system for WiFi-LTE environments. ATOM introduces two groundbreaking components: (i) A network interface selection algorithm that intelligently allocates user traffic between WiFi and LTE to enhance QoE and (ii) an interface switching service that ensures the smooth redirection of ongoing user sessions in a cost-efficient and standards-compliant manner. Through rigorous testing on a live LTE-WiFi testbed, utilizing YouTube traffic as a benchmark, we demonstrate that ATOM significantly reduces video buffering delays by a factor of 3-4 times when compared to conventional methods.

We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints connectivity and biconnectivity, and one optimization objective maximum power used. We present two centralized algorithms for use in static networks, and prove their optimality. For mobile networks, we present two distributed heuristics that adaptively adjust node transmit powers in response to topological changes and attempt to maintain a connected topology using minimum power. We analyze the throughput, delay, and power consumption of our algorithms using a prototype software implementation, an emulation of a power-controllable radio, and a detailed channel model. Our results show that the performance of multihop wireless networks in practice can be substantially increased with topology control.

This paper presents an overview of radio interface technologies for cooperative transmission in 3GPP LTE-Advanced, i.e., coordinated multi-point (CoMP) transmission, enhanced inter-cell interference coordination (eICIC) for heterogeneous deployments, and relay transmission techniques. This paper covers not only the technical components in the 3GPP specifications that have already been released, but also those that were discussed in the Study Item phase of LTE-Advanced, and those that are currently being discussed in 3GPP for potential specification in future LTE releases. key words: LTE-Advanced, CoMP, eICIC, relay

Network Service Providers should offer provisioning services with guaranteed Quality of Service (QoS), specifically adapted to the characteristics of the applications running on their network. In this paper we propose the Network Control Layer (NCL), a software framework solution based on Software Defined Networks (SDN), OpenFlow and Network as a Service (NaaS) paradigms. It addresses a major innovation area in the field of network control and management providing on-demand end-to-end network provisioning services with guaranteed QoS, based on the specific requirements of on-top running interactive applications. The NCL implementation is based on the OpenNaaS framework, and it includes mechanisms for network status monitoring and SDN switches configuration based on the interactive applications' QoS network requirements. We demonstrate NCL's utility in the context of control plane models making use of a practical use case.

The increase in WiFi physical layer transmission speeds from 1~Mbps to 1 Gbps has reduced transmission times for a 1500 byte packet from 12 ms to 12 us. However, WiFi MAC overheads such as channel access and acks have not seen similar reductions and cumulatively contribute about 150 us on average per packet. Thus, the efficiency of WiFi has deteriorated from over 80% at 1 Mbps to under 10% at 1 Gbps.
 In this paper, we propose WiFi-Nano, a system that uses 800 ns slots} to significantly improve WiFi efficiency. Reducing slot time from 9 us to 800 ns makes backoffs efficient, but clear channel assessment can no longer be completed in one slot since preamble detection can now take multiple slots. Instead of waiting for multiple slots for detecting preambles, nodes speculatively transmit preambles as their backoff counters expire, while continuing to detect premables using self-interference cancellation. Upon detection of preambles from other transmitters, nodes simply abort their own preamble transmissions, thereby allowing the earliest transmitter to succeed. Further, receivers speculatively transmit their ack preambles at the end of packet reception, thereby reducing ack overhead. We validate the effectiveness of WiFi-Nano through implementation on an FPGA-based software defined radio platform, and through extensive simulations, demonstrate efficiency gains of up to 100%.

1389-1286/$ see front matter 2010 Elsevier B.V doi:10.1016/j.comnet.2010.02.006 * Corresponding author. Tel.: +61 3 99053503; fa E-mail address: ASekerci@ieee.org (Y. Ahmet S ek Vertical handover decision (VHD) algorithms are essential components of the architecture of the forthcoming Fourth Generation (4G) heterogeneous wireless networks. These algorithms need to be designed to provide the required Quality of Service (QoS) to a wide range of applications while allowing seamless roaming among a multitude of access network technologies. In this paper, we present a comprehensive survey of the VHD algorithms designed to satisfy these requirements. To offer a systematic comparison, we categorize the algorithms into four groups based on the main handover decision criterion used. Also, to evaluate tradeoffs between their complexity of implementation and efficiency, we discuss three representative VHD algorithms in each group. 2010 Elsevier B.V. All rights reserved.

Multi-hop networks are expected to be an important part of 5G mobile systems. In a multi-hop cellular network (MCN), the nodes assist each other in relaying packets towards their destinations. The structures and operations of multi-hop networks make them particularly vulnerable to certain types of attack. Hence, security measures to counter these attacks are necessary. In this paper, we provide an overview of the secure routing protocols for multi-hop networks and classify them into MCN Type-1 and Type-0 categories for device-to-device communications, and the Internet-of-Things category for machine-to-machine communications. Our focus is on the applied cryptographic techniques and the security mechanisms in secure routing protocols. We propose an evaluation framework incorporating different security aspects, vulnerabilities, and levels of deployability to compare a number of secure routing protocols. Moreover, we review the secure routing paths in software-defined networking as a solution for existing challenges in multi-hop networks. Some open research problems are highlighted as possible directions for future studies.

The TCP protocol is used by the majority of the network applications on the Internet. TCP performance is strongly influenced by its congestion control algorithms that limit the amount of transmitted traffic based on the estimated network capacity and utilization. Because the freely available Linux operating system has gained popularity especially in the network servers, its TCP implementation affects many of the network interactions carried out today. We describe the fundamentals of the Linux TCP design, concentrating on the congestion control algorithms. The Linux TCP implementation supports SACK, TCP timestamps, Explicit Congestion Notification, and techniques to undo congestion window adjustments after incorrect congestion notifications. In addition to features specified by IETF, Linux has implementation details beyond the specifications aimed to further improve its performance. We discuss these, and finally show the performance effects of Quick acknowledgements, Rate-halving, and the algorithms for correcting incorrect congestion window adjustments by comparing the performance of Linux TCP implementing these features to the performance achieved with an implementation that does not use the algorithms in question.

Mobile Cloud Computing (MCC) has draw extensive research attentions due to the increasingly demand on the energy consumption and execution time constraints on Mobile Devices (MDs). MCC has been well investigated in the scenarios when workload is offloaded to a remote cloud or a cloudlet. However, in those scenarios, infrastructure is required to provide connections between MDs and the cloud#x002F;cloudlet. To facilitate MCC for environment when infrastructure is not available, Ad-hoc MCC which allows MDs to share and process workload coordinately is discussed [1]. In this study, the detailed resource allocation problem in Ad-hoc MCC, including how to assign tasks to MDs, how tasks are executed on each MD, and how to arrange task and task result return transmissions so that interference among different MDs can be avoided are carefully studied. Since the problem is NP-hard, a heuristic algorithm is then introduced. As shown in the evaluation section, when no infrastructure is available, compared with the case when an application is executed solely on a single MD, the proposed algorithm can reduce an application's response time significantly.

We propose an efficient client-based approach for channel management (channel assignment and load balancing) in 802.11-based WLANs that lead to better usage of the wireless spectrum. This approach is based on a “conflict set coloring” formulation that jointly performs load balancing along with channel assignment. Such a formulation has a number of advantages. First, it explicitly captures interference effects at clients. Next, it intrinsically exposes opportunities for better channel re-use. Finally, algorithms based on this formulation do not depend on specific physical RF models and hence can be applied efficiently to a wide-range of inbuilding as well as outdoor scenarios. We have performed extensive packet-level simulations and measurements on a deployed wireless testbed of 70 APs to validate the performance of our proposed algorithms. We show that in addition to single network scenarios, the conflict set coloring formulation is well suited for channel assignment where multiple wireless networks share and contend for spectrum in the same physical space. Our results over a wide range of both simulated topologies and in-building testbed experiments indicate that our approach improves application level performance at the clients by upto three times (and atleast 50%) in comparison to current best-known techniques.

We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into  $K$  orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.


Question: Applying MPTCP subflow association control for the optimization of heterogeneous wireless networks.

This paper explores the feasibility of implementing a centralized indoor radio communications system for an entire building using a single antenna. While this concept initially seems promising, it is constrained by signal attenuation and multipath delay spread, rendering it suitable only for small buildings and narrow-band FDMA systems with limited reliability and adaptability. We present findings from indoor propagation measurements that highlight two alternative signal distribution strategies, significantly enhancing system performance. The first approach involves dividing the building into numerous small cells, each serviced by a centrally located antenna and operating on distinct frequency bands to avoid interference. The second strategy involves creating one or more large cells served by a distributed antenna system, such as a "leaky feeder," which winds through the building's corridors. This method overcomes the frequency handoff issue characteristic of the first approach, while still achieving substantial reductions in signal attenuation and multipath delay spread compared to a centralized system. Our measurements reveal that both approaches can decrease signal attenuation by several tens of decibels and limit the rms delay spread to 20 to 50 ns, even in expansive buildings. These enhancements pave the way for the implementation of advanced broad-band TDMA systems that are adaptable, resilient, and adaptable to various building structures.

**Question**: Developing a novel approach to handle the valve-point effect in the economic dispatch problem using a direct search method.

**Documents**: A Hybrid Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design
This paper introduces a hybrid evolutionary learning algorithm, combining genetic algorithm (GA) and particle swarm optimization (PSO), termed HGAPSO, for the design of recurrent neural/fuzzy networks. HGAPSO innovates by not only utilizing crossover and mutation operations from GA but also integrating PSO, which is applied to the best-performing individuals or "elites." These elites undergo enhancement through PSO, simulating the maturation process in nature. In the new generation, half the population consists of these enhanced elites, while the remaining individuals are produced through crossover and mutation operations on these elites. The proposed HGAPSO is applied to recurrent neural network design, where a fully connected network is utilized for a temporal sequence production problem, and recurrent fuzzy network design, employing a Takagi-Sugeno-Kang-type architecture for dynamic plant control. Comparative analysis against GA and PSO demonstrates HGAPSO's superior performance in recurrent network design.

**Similar Documents**: This work presents a novel evolutionary learning algorithm, HGAPSO, which merges the strengths of genetic algorithm (GA) and particle swarm optimization (PSO) for the design of recurrent neural

Dense, unmanaged IEEE 802.11 deployments tempt saboteurs into launching jamming attacks by injecting malicious interference. Nowadays, jammers can be portable devices that transmit intermittently at low power in order to conserve energy. In this paper, we first conduct extensive experiments on an indoor 802.11 network to assess the ability of two physical-layer functions, rate adaptation and power control, in mitigating jamming. In the presence of a jammer, we find that: 1) the use of popular rate adaptation algorithms can significantly degrade network performance; and 2) appropriate tuning of the carrier sensing threshold allows a transmitter to send packets even when being jammed and enables a receiver to capture the desired signal. Based on our findings, we build ARES, an Anti-jamming REinforcement System, which tunes the parameters of rate adaptation and power control to improve the performance in the presence of jammers. ARES ensures that operations under benign conditions are unaffected. To demonstrate the effectiveness and generality of ARES, we evaluate it in three wireless test-beds: 1) an 802.11n WLAN with MIMO nodes; 2) an 802.11a/g mesh network with mobile jammers; and 3) an 802.11a WLAN with TCP traffic. We observe that ARES improves the network throughput across all testbeds by up to 150%.

As consumer-level cameras are becoming cheaper and cheaper, the scene of interest can now be captured by multiple cameras simultaneously from different viewpoints. By transmitting texture and depth maps of the views captured using two adjacent cameras, users can synthesize any view in between using depth image based rendering (DIBR) technique such as 3D-warping. This provides users with continuous view angles and is key technique for Free Viewpoint TV (FTV), etc. In this scenario, multiple views must be delivered through bandwidth-limited wireless networks, how to adaptive reduce the source coding rate to fit the bandwidth becomes an important issue. The standard multi-view video codec H.264 MVC explores the inter-view and intra-view correlation to reduce source encoding rate. But all views are required at the client side for decoding any single view, which is apparently not transmission efficient. A better solution is using H.264 to differentially encode the two adjacent views separately, but still this requires double-folder bandwidth comparing to the traditional video streaming. Multiple description coding (MDC) encodes one view into multiple descriptions for robust transmission. This inspires another option: sending only part of each view. This can greatly reduce the source encoding rate, but interpolating skipped frames before synthesizing the middle view consumes mobile devices' precious battery life. Whether to send at a lower source encoding rate but with extra energy consumption or send at a high source encoding rate is nontrivial, and has not been studied in any formal way. In this paper, we investigated both source encoding rate reduction and energy consumption with the bandwidth constraint. We formulated the problem into an optimization problem, i.e., maximizing the system utility, which is defined as the received video quality minus the weighted energy consumption. Simulation results showed that our proposed scheme provides a significant advantage over competing schemes in typical network conditions.

In this paper, we evaluate handover (HO) performance when we apply various time-to-trigger (TTT) methods in self-organizing network (SON)-based long term evolution (LTE) systems. Although TTT can mitigate the wasteful ping-pong HO effect, it also can cause undesirable radio link failure (RLF) due to delayed HO. The optimal HO timings that produce the lowest ping-pong rate within allowable RLF rate vary depending on user equipment (UE) speeds and neighboring cell configurations. To achieve efficient HO timings, we propose and investigate two methods: “adaptive” and “grouping.” In the “adaptive method,” we select the adaptive TTT value for each UE speed based on RLF rate of 2%. The “grouping method” classifies UE speeds into three ranges and assigns the proper TTT value to each range. To apply the LTE specification more effectively, we suggest the criteria of grouping, and propose the proper TTT value for each range. We consider HO in two neighboring cell configurations: from a macro cell to either a macro cell or a pico cell. Simulation results show that the HO performance of the “adaptive method” is greatly improved compared to that of applying fixed TTT values. The results also show that the performance of the “grouping method”, using proposed criteria and proper TTT values, is comparable to that of the “adaptive method.”

Wireless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g. a vehicle's speed and location, or a traffic signal's state and timing). The aggregate of these uncoordinated broadcasts will lead to channel congestion under dense traffic scenarios, with a resulting reduction in the effectiveness of the collision avoidance applications making use of the transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology for deployment. This paper defines a new congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This "perfect" convergence avoids the limit cycle behavior inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including: guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust stability under all conditions, convergence with asynchronous updates, and the implications of measurement noise for statistical properties of convergence. The paper illustrates key analytical results using MATLAB numerical results, and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high density scenarios.

As the number of wireless devices grows, the increasing demand for the shared radio frequency (RF) spectrum becomes a critical problem. Unlike wired communications in which, theoretically, more fibers can be used to accommodate the increasing bandwidth demand, wireless spectrum cannot be arbitrarily increased due to the fundamental limitations imposed by the physical laws. On the other hand, recent advances in free space optical (FSO) technologies promise a complementary approach to increase wireless capacity. However, high-speed FSO technologies are currently confined to short distance transmissions, resulting in limited mobility. In this paper, we briefly describe WiFO, a hybrid WiFi-FSO network for Gbps wireless local area network (WLAN) femtocells that can provide up to one Gbps per user while maintaining seamless mobility. While typical RF femtocells are non-overlapped to minimize inter-cell interference, there are advantages of using overlapped femtocells to increase mobility and throughput when the number of users is small. That said, the primary contribution of this paper will be a novel location assisted coding (LAC) technique used in the WiFO network that aims to increase throughput and reduce interference for multiple users in a dense array of femtocells. Both theoretical analysis and numerical experiments show orders of magnitude increase in throughput using LAC over basic codes.

The upgrade of a legacy network to a full software-defined networking (SDN) deployment is usually an incremental process, during which SDN switches and legacy switches coexist in the hybrid network. However, with inappropriate deployment of SDN switches and design of hybrid control, the advantages of SDN control could not exert, and it even results in performance degradation or inconsistency (e.g., loops, black-holes). Therefore, the hybrid SDN requires considerable coordination of the centralized control and distributed routing. In this paper, we propose a solution to handle the heterogeneity caused by distinct forwarding characteristics of SDN and legacy switches, therefore boosting the benefits of hybrid SDN. We plan SDN placement to enhance the SDN controllability over the hybrid network, and conduct traffic engineering considering both the forwarding characteristics of SDN and legacy switches. The experiments with various topologies show that the SDN placement planning and hybrid forwarding yield better network performance especially in the early 70% SDN deployment.

This paper provides an in-depth view on the technologies being considered for Long Term Evolution-Advanced (LTE-Advanced). First, the evolution from third generation (3G) to fourth generation (4G) is described in terms of performance requirements and main characteristics. The new network architecture developed by the Third Generation Partnership Project (3GPP), which supports the integration of current and future radio access technologies, is highlighted. Then, the main technologies for LTE-Advanced are explained, together with possible improvements, their associated challenges, and some approaches that have been considered to tackle those challenges.

Wireless networking protocols are increasingly being designed to exploit a user's measured channel condition; we call such protocols channel-aware. Each user reports the measured channel condition to a manager of wireless resources and a channel-aware protocol uses these reports to determine how resources are allocated to users. In a channel-aware protocol, each user's reported channel condition affects the performance of every other user. The deployment of channel-aware protocols increases the risks posed by false channel-condition feedback. In this paper, we study what happens in the presence of an attacker that falsely reports its channel condition. We perform case studies on channel-aware network protocols to understand how an attack can use false feedback and how much the attack can affect network performance. The results of the case studies show that we need a secure channel condition estimation algorithm to fundamentally defend against the channel-condition misreporting attack. We design such an algorithm and evaluate our algorithm through analysis and simulation. Our evaluation quantifies the effect of our algorithm on system performance as well as the security and the performance of our algorithm.

We consider the channel assignment problem in a multi-radio wireless mesh network that involves assigning channels to radio interfaces for achieving efficient channel utilization. We propose the notion of a traffic-independent base channel assignment to ease coordination and enable dynamic, efficient and flexible channel assignment. We present a novel formulation of the base channel assignment as a topology control problem, and show that the resulting optimization problem is NP-complete. We then develop a new greedy heuristic channel assignment algorithm (termed CLICA) for finding connected, low interference topologies by utilizing multiple channels. Our extensive simulation studies show that the proposed CLICA algorithm can provide large reduction in interference (even with a small number of radios per node), which in turn leads to significant gains in both link layer and multihop performance in 802.11-based multi-radio mesh networks.

Device-to-Device (D2D) communication will become an important technology in future networks with the increase of the requirements of local communication services. The interference between cellular communication and D2D communication can be coordinated by proper power control and resource allocation. In this paper, we analyze the resource allocation methods for D2D communication underlaying cellular networks. A novel resource allocation method that D2D can reuse the resources of more than one cellular user is proposed. After that, we discuss the selection of the optimal resource allocation method from the proposed method and the conventional methods. Finally, the performance of different methods is evaluated through numerical simulation. The simulation results show that the proposed method is the optimal resource allocation method when the D2D pair locates at the most part of the cell area in both uplink and downlink. The proposed method can improve the sum throughput of cellular communication and D2D communication significantly.


Question: Employing MPTCP subflow association control for the optimization of heterogeneous wireless networks.

This paper delves into the design, implementation, and evaluation of a congestion control mechanism specifically tailored for Multipath TCP (MPTCP). MPTCP, as defined by the IETF's mptcp working group, enables the division of a single data stream across various network paths. This feature not only enhances reliability but also optimizes resource utilization within the network. We detail the development of a congestion control algorithm for MPTCP, its integration into the Linux operating system, and the subsequent performance evaluation across multihomed servers, data centers, and mobile devices. Our findings highlight that certain intuitive approaches to multipath congestion control can be counterproductive, yet our proposed algorithm outperforms single-path TCP in terms of throughput and fairness. Notably, our congestion control mechanism can seamlessly replace existing TCP protocols, ensuring safe and efficient deployment in various network environments.

IEEE 802.11 Medium Access Control(MAC) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is Distributed Coordination Function(DCF), which is a Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA) with binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Comparing with other models, this model is shown to be able to predict the behaviors of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.

Service Function Chains (SFCs) comprise a sequence of Network Functions (NFs) that are typically traversed in-order by data flows. Consequently, SFC delay grows linearly with the length of the SFC. Yet, for highly latency sensitive applications, this delay may be unacceptable---particularly when the constituent NFs are virtualized, running on commodity servers. In this paper, we investigate how SFC latency may be reduced by exploiting opportunities for parallel packet processing across NFs. We propose ParaBox, a novel hybrid packet processing architecture that, when possible, dynamically distributes packets to VNFs in parallel and merges their outputs intelligently to ensure the preservation of correct sequential processing semantics. To demonstrate the feasibility of our approach, we implement a ParaBox prototype on top of the DPDK-enabled Berkeley Extensible Software Switch. Our preliminary experiment results show that ParaBox can not only significantly reduce the service chaining latency, but also improve throughput.

We present the design, implementation, and evaluation of B4, a private WAN connecting Google's data centers across the planet. B4 has a number of unique characteristics: i) massive bandwidth requirements deployed to a modest number of sites, ii) elastic traffic demand that seeks to maximize average bandwidth, and iii) full control over the edge servers and network, which enables rate limiting and demand measurement at the edge.
 These characteristics led to a Software Defined Networking architecture using OpenFlow to control relatively simple switches built from merchant silicon. B4's centralized traffic engineering service drives links to near 100% utilization, while splitting application flows among multiple paths to balance capacity against application priority/demands. We describe experience with three years of B4 production deployment, lessons learned, and areas for future work.

We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into  $K$  orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.

Multi-hop networks are expected to be an important part of 5G mobile systems. In a multi-hop cellular network (MCN), the nodes assist each other in relaying packets towards their destinations. The structures and operations of multi-hop networks make them particularly vulnerable to certain types of attack. Hence, security measures to counter these attacks are necessary. In this paper, we provide an overview of the secure routing protocols for multi-hop networks and classify them into MCN Type-1 and Type-0 categories for device-to-device communications, and the Internet-of-Things category for machine-to-machine communications. Our focus is on the applied cryptographic techniques and the security mechanisms in secure routing protocols. We propose an evaluation framework incorporating different security aspects, vulnerabilities, and levels of deployability to compare a number of secure routing protocols. Moreover, we review the secure routing paths in software-defined networking as a solution for existing challenges in multi-hop networks. Some open research problems are highlighted as possible directions for future studies.

While cloud computing providers offer guaranteed allocations for resources such as CPU and memory, they do not offer any guarantees for network resources. The lack of network guarantees prevents tenants from predicting lower bounds on the performance of their applications. The research community has recognized this limitation but, unfortunately, prior solutions have significant limitations: either they are inefficient, because they are not work-conserving, or they are impractical, because they require expensive switch support or congestion-free network cores.
 In this paper, we propose ElasticSwitch, an efficient and practical approach for providing bandwidth guarantees. ElasticSwitch is efficient because it utilizes the spare bandwidth from unreserved capacity or underutilized reservations. ElasticSwitch is practical because it can be fully implemented in hypervisors, without requiring a specific topology or any support from switches. Because hypervisors operate mostly independently, there is no need for complex coordination between them or with a central controller. Our experiments, with a prototype implementation on a 100-server testbed, demonstrate that ElasticSwitch provides bandwidth guarantees and is work-conserving, even in challenging situations.

High Definition video streaming over WLANs faces many challenges because video data requires not only data integrity but also frames have strict playout deadline. Traditional streaming methods that rely solely on either UDP or TCP have difficulties meeting both requirements because UDP incurs packet loss while TCP incurs delay. This paper proposed a new streaming method called Flexible Dual-TCP/UDP Streaming Protocol (FDSP) that utilizes the benefit of both UDP and TCP. The FDSP takes advantage of the hierarchical structure of the H.264/AVC syntax and uses TCP to transmit important syntax elements of H.264/AVC video and UDP to transmit non-important elements. The proposed FDSP is implemented and validated under different wireless network conditions. Both visual quality and delay results are compared against pure-UDP and pure-TCP streaming methods. Our results show that FDSP effectively achieves a balance between delay and visual quality, thus it has advantage over traditional pure-UDP and pure-TCP methods.

Mobile Cloud Computing (MCC) has draw extensive research attentions due to the increasingly demand on the energy consumption and execution time constraints on Mobile Devices (MDs). MCC has been well investigated in the scenarios when workload is offloaded to a remote cloud or a cloudlet. However, in those scenarios, infrastructure is required to provide connections between MDs and the cloud#x002F;cloudlet. To facilitate MCC for environment when infrastructure is not available, Ad-hoc MCC which allows MDs to share and process workload coordinately is discussed [1]. In this study, the detailed resource allocation problem in Ad-hoc MCC, including how to assign tasks to MDs, how tasks are executed on each MD, and how to arrange task and task result return transmissions so that interference among different MDs can be avoided are carefully studied. Since the problem is NP-hard, a heuristic algorithm is then introduced. As shown in the evaluation section, when no infrastructure is available, compared with the case when an application is executed solely on a single MD, the proposed algorithm can reduce an application's response time significantly.

1 Manuscript submitted to ICDCS 2005 on October 8, 2004. All rights reserved by the authors. This research was supported by an NSF ITR Research Grant under contract number ACI-0325409. Corresponding Author: Kai Hwang, Email: kaihwang@usc.edu, Tel: 213-740-4470, Fax: 213-740-4418. Abstract—High availability in network services is crucial for effective large-scale distributed computing. While denial-ofservice (DoS) attacks through massive packet flooding have baffled researchers for years, a new type of even more detrimental attack—shrew attacks (periodic intensive packet bursts with low average rate)—has recently been identified. Shrew attacks can significantly degrade well-behaved TCP sessions and are very difficult to detect, not to mention defend against, due to its low average rate. We propose a new stateful adaptive queue management technique called HAWK (Halting Anomaly with Weighted choKing) which works by judiciously identifying malicious shrew packet flows using a small flow table and dropping such packets decisively to halt the attack such that well-behaved TCP sessions can re-gain their bandwidth shares. Our NS-2 based extensive performance results indicate that HAWK is highly agile in the sense that it can swiftly (e.g., within 5 seconds) identify and then halt shrew attacks with a flow table of tiny size (e.g., 600 bytes). Thus, HAWK is readily amenable to practical implementation in a real environment.

Increasing need for people to be "connected"; while at the same time remain as mobile as ever poses several interesting issues in wireless networks. It is conceivable in the near-future that wireless "hotspots" experience flash crowds-like traffic arrival pattern. A common phenomena in the Internet today characterized by sudden and unpredicted increase in popularity of on-line content. In this paper, we propose SPAWN, a cooperative strategy for content delivery and sharing in future vehicular networks. We study the issues involved in using such a strategy from the standpoint of vehicular ad-hoc networks. In particular, we show that not only content server but also wireless access network load reduction is critical. We propose a "communication efficient" swarming protocol which uses a gossip mechanism that leverages the inherent broadcast nature of the wireless medium, and a piece-selection strategy that takes proximity into account in decisions to exchange pieces. We show through simulation that gossip incorporates location-awareness into peer selection, while incurring low messaging overhead, and consequently enhancing the swarming protocol performance. We develop an analytical model to characterize the performance of SPAWN.


Question: Employing MPTCP subflow association control for the optimization of heterogeneous wireless networks.

This paper discusses a control mechanism for rates in communication networks, focusing on shadow prices, proportional fairness, and stability. The authors, F. P. Kelly, A. K. Maulloo, and D. K. H. Tan, explore how rate control can be optimized to achieve fairness among users while maintaining network stability. Their approach is grounded in the concept of shadow prices, a term used in economics to represent the value of a good or service when it is not sold in a market. In the context of communication networks, shadow prices can be used to allocate resources in a way that reflects the true cost of transmission, leading to a more equitable distribution of bandwidth among users. Proportional fairness is another key element of their strategy, aiming to ensure that no user's rate is reduced disproportionately in comparison to others. This method not only enhances user satisfaction but also contributes to the overall stability of the network by preventing any single user from monopolizing the resources. The authors' work, published in the Journal of the Operational Research Society in 1998, has been influential in shaping modern network management practices, particularly in the realm of congestion control and resource allocation. Their insights into shadow prices and proportional fairness have been widely adopted in the design of algorithms for rate control in both wired and wireless networks, helping to improve the efficiency and fairness of data transmission.

1 Manuscript submitted to ICDCS 2005 on October 8, 2004. All rights reserved by the authors. This research was supported by an NSF ITR Research Grant under contract number ACI-0325409. Corresponding Author: Kai Hwang, Email: kaihwang@usc.edu, Tel: 213-740-4470, Fax: 213-740-4418. Abstract—High availability in network services is crucial for effective large-scale distributed computing. While denial-ofservice (DoS) attacks through massive packet flooding have baffled researchers for years, a new type of even more detrimental attack—shrew attacks (periodic intensive packet bursts with low average rate)—has recently been identified. Shrew attacks can significantly degrade well-behaved TCP sessions and are very difficult to detect, not to mention defend against, due to its low average rate. We propose a new stateful adaptive queue management technique called HAWK (Halting Anomaly with Weighted choKing) which works by judiciously identifying malicious shrew packet flows using a small flow table and dropping such packets decisively to halt the attack such that well-behaved TCP sessions can re-gain their bandwidth shares. Our NS-2 based extensive performance results indicate that HAWK is highly agile in the sense that it can swiftly (e.g., within 5 seconds) identify and then halt shrew attacks with a flow table of tiny size (e.g., 600 bytes). Thus, HAWK is readily amenable to practical implementation in a real environment.

A testbed implementation for an Ethernet fronthaul transporting signals arising from a long-term evolution (LTE) functional subdivision ("split") at the media-access control (MAC)/physical layer (PHY) interface is presented. Based on open LTE base station software, the testbed demonstrates significant data rate reductions compared to current fronthaul implementations that rely on In- phase and Quadrature radio sample transportation and data rates that scale with cell load. All generated traffic flows are clearly distinguishable using appropriate packet headers. A selection of test cases and their corresponding results are presented to demonstrate the operation of the fronthaul and the performance of individual flows in terms of data rates and overheads.

We consider the problem of routing in intermittently connected networks. In such networks there is no guarantee that a fully connected path between source and destination exist at any time, rendering traditional routing protocols unable to deliver messages between hosts. We propose a probabilistic routing protocol for such networks.

We present a new protocol for power control in ad hoc networks. We describe the issues in conceptualizing the power control problem, and provide an architecturally simple as well as theoretically well founded solution. The solution is shown to simultaneously satisfy the three objectives of maximizing the traffic carrying capacity of the entire network, extending battery life through providing low power routes, and reducing the contention at the MAC layer. Further, the protocol has the plug and play feature that it can be employed in conjunction with any routing protocol that pro-actively maintains a routing table. The protocol, called COMPOW, has been implemented in the Linux kernel and we describe the software architecture and implementation details.

Wireless sensor networks (WSNs) are being deployed for an ever-widening range of applications, particularly due to their low power consumption and reliability. In most applications, the sensor data must be sent over the Internet or core network, which results in the WSN backhaul problem. Passive optical networks (PONs), a next-generation access network technology, are well suited form a backhaul which interfaces WSNs to the core network. In this paper a new structure is proposed which converges WSNs and PONs. It is modelled and analyzed through queuing theory by employing two M/M/1 queues in tandem. Numerical results show how the WSN and PON dimensions affect the average queue length, thus serving as a guideline for future resource allocation and scheduling of such a converged network.

Multi-user MIMO (MU-MIMO) networks reveal the unique opportunities arising from a joint optimization of antenna combining techniques with resource allocation protocols. Furthermore, it brings robustness with respect to multipath richness, allowing for compact antenna spacing at the BS and, crucially, yielding the diversity and multiplexing gains without the need for multiple antenna user terminals. To realize these gains, however, the BS should be informed with the user's channel coefficients, which may limit practical application to TDD or low-mobility settings. To circumvent this problem and reduce feedback load, combining MU-MIMO with opportunistic scheduling seems a promising direction. The success for this type of scheduler is strongly traffic and QoS-dependent, however.

Thanks to the convergence of pervasive mobile communications and fast-growing online social networking, mobile social networking is penetrating into our everyday life. Aiming to develop a systematic understanding of the interplay between social structure and mobile communications, in this paper we exploit social ties in human social networks to enhance cooperative device-to-device communications. Specifically, as hand-held devices are carried by human beings, we leverage two key social phenomena, namely social trust and social reciprocity, to promote efficient cooperation among devices. With this insight, we develop a coalitional game theoretic framework to devise social-tie based cooperation strategies for device-to-device communications. We also develop a network assisted relay selection mechanism to implement the coalitional game solution, and show that the mechanism is immune to group deviations, individually rational, and truthful. We evaluate the performance of the mechanism by using real social data traces. Numerical results show that the proposed mechanism can achieve up-to 122% performance gain over the case without D2D cooperation.

Multicast transmission, wherein the same packet must be delivered to multiple receivers, is an important aspect of sensor and tactical networks and has several distinctive traits as opposed to more commonly studied unicast networks. Specially, these include 1) identical packets must be delivered successfully to several nodes, 2) outage at any receiver requires the packet to be retransmitted at least to that receiver, and 3) the multicast rate is dominated by the receiver with the weakest link in order to minimize outage and retransmission. A first contribution of this paper is the development of a tractable multicast model and throughput metric that captures each of these key traits in a multicast wireless network. We utilize a Poisson cluster process (PCP) consisting of a distinct Poisson point process (PPP) for the transmitters and receivers, and then define the multicast transmission capacity (MTC) as the maximum achievable multicast rate per transmission attempt times the maximum intensity of multicast clusters under decoding delay and multicast outage constraints. A multicast cluster is a contiguous area over which a packet is multicasted, and to reduce outage it can be tessellated into v smaller regions of multicast. The second contribution of the paper is the analysis of several key aspects of this model, for which we develop the following main result. Assuming τ/v transmission attempts are allowed for each tessellated region in a multicast cluster, we show that the MTC is Θ(ρkxlog(k)vy) where ρ, x and y are functions of τ and v depending on the network size and intensity, and k is the average number of the intended receivers in a cluster. We derive {ρ, x, y} for a number of regimes of interest, and also show that an appropriate number of retransmissions can significantly enhance the MTC.

Service Function Chains (SFCs) comprise a sequence of Network Functions (NFs) that are typically traversed in-order by data flows. Consequently, SFC delay grows linearly with the length of the SFC. Yet, for highly latency sensitive applications, this delay may be unacceptable---particularly when the constituent NFs are virtualized, running on commodity servers. In this paper, we investigate how SFC latency may be reduced by exploiting opportunities for parallel packet processing across NFs. We propose ParaBox, a novel hybrid packet processing architecture that, when possible, dynamically distributes packets to VNFs in parallel and merges their outputs intelligently to ensure the preservation of correct sequential processing semantics. To demonstrate the feasibility of our approach, we implement a ParaBox prototype on top of the DPDK-enabled Berkeley Extensible Software Switch. Our preliminary experiment results show that ParaBox can not only significantly reduce the service chaining latency, but also improve throughput.

Internet video makes up a significant part of the Internet traffic and its fraction is constantly growing. In order to guarantee best user experience throughout different network access technologies with dynamically varying network conditions, it is fundamental to adopt technologies enabling a proper delivery of the media content. One of such technologies is adaptive streaming. It allows to dynamically adapt the bit-rate of the stream to varying network conditions. There are various approaches to adaptive streaming. In our work, we focus on the receiver-driven approach where the media file is subdivided into segments, each of the segments is provided at multiple bit-rates, and the task of the client is to select the appropriate bit-rate for each of the segments. With this approach, the challenges are (i) to properly estimate the dynamics of the available network throughput, (ii) to control the filling level of the client buffer in order to avoid underflows resulting in playback interruptions, (iii) to maximize the quality of the stream, while avoiding unnecessary quality fluctuations, and, finally, (iv) to minimize the delay between the user's request and the start of the playback. During our work, we designed and implemented a receiver-driven adaptation algorithm for adaptive streaming that does not rely on cross-layer information or server assistance. We integrated the algorithm with a prototype implementation of a streaming client based on the MPEG DASH (Dynamic Adaptive Streaming over HTTP) standard. We evaluated the implemented prototype in real-world scenarios and found that it performes remarkably well even under challenging network conditions. Further, it exhibits stable and fair operation if a common link is shared among multiple clients.


Question: DroidDet: Accurate and sturdy identification of Android malware utilizing static analysis combined with the rotation forest model.

Innovative Hybrid Malware Detector: Integrating Static and Dynamic Analysis for Enhanced Security

With the escalating threat of malware to computer systems and networks, there is a pressing need for robust detection mechanisms that can effectively counteract these harmful software programs. Traditional signature-based detection methods, predominantly used in commercial antivirus software, have proven insufficient in identifying new, previously unseen malware. To address this limitation, supervised machine learning techniques have emerged as a viable solution. These techniques leverage two primary types of features: static and dynamic. Static features are derived without executing the malware sample, while dynamic features require the software to run, thereby capturing its behavior in action.

In this paper, we introduce a groundbreaking hybrid malware detection system, coined as OPEM. This system ingeniously combines static analysis, specifically the frequency of operational code occurrences, with dynamic analysis, utilizing the execution trace of an executable file. By merging these two approaches, OPEM aims to overcome the limitations inherent in either technique when used in isolation. Our research demonstrates that this hybrid strategy significantly improves detection rates and overall performance compared to employing static or dynamic analysis separately.

OPEM represents a significant advancement in the field of malware detection, offering a more comprehensive and effective solution to the ever-evolving threat landscape. By seamlessly integrating static and dynamic analysis, OPEM provides a robust defense against malware, enhancing the security of computer systems and networks against potential attacks.

Internet and internet users are increasing day by day. Also due to rapid development of internet technology , security is becoming big issue. Intruders are monitoring comput er network continuously for attacks. A sophisticated firewall with efficient intrusion detection system (IDS) is required to pre vent computer network from attacks. A comprehensive study of litera tures proves that data mining techniques are more powerful techni que to develop IDS as a classifier. Performance of classif ier is a crucial issue in terms of its efficiency, also number of fe ature to be scanned by the IDS should also be optimized. In thi s paper two techniques C5.0 and artificial neural network (ANN) ar e utilized with feature selection. Feature selection techniques will discard some irrelevant features while C5.0 and ANN acts as a classifier to classify the data in either normal type or one of t he five types of attack.KDD99 data set is used to train and test the models ,C5.0 model with numbers of features is producing better r sults with all most 100% accuracy. Performances were also verified in terms of data partition size.

Malware for current smartphone platforms is becoming increasingly sophisticated. The presence of advanced networking and sensing functions in the device is giving rise to a new generation of targeted malware characterized by a more situational awareness, in which decisions are made on the basis of factors such as the device location, the user profile, or the presence of other apps. This complicates behavioral detection, as the analyst must reproduce very specific activation conditions in order to trigger malicious payloads. In this paper, we propose a system that addresses this problem by relying on stochastic models of usage and context events derived from real user traces. By incorporating the behavioral particularities of a given user, our scheme provides a solution for detecting malware targeting such a specific user. Our results show that the properties of these models follow a power-law distribution: a fact that facilitates an efficient generation of automatic testing patterns tailored for individual users, when done in conjunction with a cloud infrastructure supporting device cloning and parallel testing. We report empirical results with various representative case studies, demonstrating the effectiveness of this approach to detect complex activation patterns.

A novel framework for SMS spam filtering is introduced in this paper to prevent mobile phone users from unsolicited SMS messages. The framework makes use of two distinct feature selection approaches based on information gain and chi-square metrics to find out discriminative features representing SMS messages. The discriminative feature subsets are then employed in two different Bayesian-based classifiers, so that SMS messages are categorized as either spam or legitimate. Moreover, the paper introduces a real-time mobile application for Android™ based mobile phones utilizing the proposed spam filtering scheme, as well. Hence, SMS spam messages are silently filtered out without disturbing phone users. Effectiveness of the filtering framework is evaluated on a large SMS message collection including legitimate and spam messages. Following the evaluation, remarkably accurate classification results are obtained for both spam and legitimate messages.

Web applications are used on a large scale worldwide, which handles sensitive personal data of users. With web application that maintains data ranging from as simple as telephone number to as important as bank account information, security is a prime point of concern. With hackers aimed to breakthrough this security using various attacks, we are focusing on SQL injection attacks and XSS attacks. SQL injection attack is very common attack that manipulates the data passing through web application to the database servers through web servers in such a way that it alters or reveals database contents. While Cross Site Scripting (XSS) attacks focuses more on view of the web application and tries to trick users that leads to security breach. We are considering three tier web applications with static and dynamic behavior, for security. Static and dynamic mapping model is created to detect anomalies in the class of SQL Injection and XSS attacks.

The mobile application (app) has become the main entrance to access the Internet on handheld devices. Unlike the Web where each webpage has a global URL to reach directly, a specific “content page” of an app can be opened only by exploring the app with several operations from the landing page. The interoperability between apps is quite fixed and thus limits the value-added “linked data” between apps. Recently, deep link has been proposed to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier (URI). However, implementing deep link for mobile apps requires a lot of manual efforts by app developers, which can be very error-prone and time-consuming. In this paper, we propose DroidLink to automatically generating deep links for existing Android apps. We design a deep link model suitable for automatic generation. Then we explore the transition of pages and build a navigation graph based on static and dynamic analysis of Android apps. Next, we realize an updating mechanism that keeps on revisiting the target app and discover new pages, and thus generates deep links for every single page of the app. Finally, we repackage the app with deep link supports, but requires no additional deployment requirements. We generate deep links for some popular apps and demonstrate the feasibility of DroidLink.

Botnets are now the key platform for many Internet attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of the current botnet detection approaches work only on specific botnet command and control (C&C) protocols (e.g., IRC) and structures (e.g., centralized), and can become ineffective as botnets change their C&C techniques. In this paper, we present a general detection framework that is independent of botnet C&C protocol and structure, and requires noa priori knowledge of botnets (such as captured bot binaries and hence the botnet signatures, and C&C server names/addresses). We start from the definition and essential properties of botnets. We define a botnet as acoordinated groupof malware instances that arecontrolled via C&C communication channels. The essential properties of a botnet are that the bots communicate with some C&C servers/peers, perform malicious activities, and do so in a similar or correlated way. Accordingly, our detection framework clusters similar communication traffic and similar malicious traffic, and performs cross cluster correlation to identify the hosts that share both similar communication patterns and similar malicious activity patterns. These hosts are thus bots in the monitored network. We have implemented our BotMiner prototype system and evaluated it using many real network traces. The results show that it can detect real-world botnets ( IRC-based, HTTP-based, and P2P botnets including Nugache and Storm worm), and has a very low false positive rate.

Mobile devices are used more and more in everyday life. They are our cameras, wallets, and keys. Basically, they embed most of our private information in our pocket. For this and other reasons, mobile devices, and in particular the software that runs on them, are considered first-class citizens in the software-vulnerabilities landscape. Several studies investigated the software-vulnerabilities phenomenon in the context of mobile apps and, more in general, mobile devices. Most of these studies focused on vulnerabilities that could affect mobile apps, while just few investigated vulnerabilities affecting the underlying platform on which mobile apps run: the Operating System (OS). Also, these studies have been run on a very limited set of vulnerabilities. In this paper we present the largest study at date investigating Android-related vulnerabilities, with a specific focus on the ones affecting the Android OS. In particular, we (i) define a detailed taxonomy of the types of Android-related vulnerability, (ii) investigate the layers and subsystems from the Android OS affected by vulnerabilities, and (iii) study the survivability of vulnerabilities (i.e., the number of days between the vulnerability introduction and its fixing). Our findings could help OS and apps developers in focusing their verification & validation activities, and researchers in building vulnerability detection tools tailored for the mobile world.

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.

Bots are malicious software components used for generating spams, launching denial of service attacks, phishing, identity theft and information exfiltration and such other illegal activities. Bot detection is an area of active research in recent times. Here we propose a bot detection mechanism for a single host. A user traffic profile is used to filter out normal traffic generated by the host. The remaining suspicious traffic is subject to detailed analysis. The nature of detailed analysis is derived from a characterization of bot traffic. The detection system is tested using two real world bots. Performance evaluation results show that the proposed system achieves a high detection rate (100%) and a low false positive rate (0–8%). The traffic filtering yielded an average reduction in traffic by 70%.

The popularity and utility of smartphones rely on their vibrant application markets; however, plagiarism threatens the long-term health of these markets. In this paper, we present a scalable approach to detecting similar Android apps based on semantic information. We implement our approach in a tool called AnDarwin and evaluate it on 265,359 apps collected from 17 markets including Google Play and numerous third-party markets. In contrast with earlier approaches, AnDarwin does not compare apps pairwise, thus greatly increasing its scalability. Additionally, AnDarwin does not rely on information other than the app code — such as the app’s market, signature, or description — thus greatly increasing its reliability. AnDarwin can automatically detect library code and remove it from the similarity analysis. We present two use cases for AnDarwin: finding similar apps by different developers (“clones”) and similar apps from the same developer (“rebranded”). In ten hours, AnDarwin detected at least 4,295 apps which have been the victims of cloning and 36,106 apps that are rebranded. By analyzing the clusters found by AnDarwin, we found 88 new variants of malware and identified 169 malicious apps based on differences in the requested permissions. In contrast to earlier approaches, AnDarwin can detect both full and partial app similarity. Additionally, AnDarwin can automatically detect similar code that is injected into many apps, which may indicate the spread of malware. Our evaluation demonstrates AnDarwin’s ability to accurately detect similar apps on a large scale.


Question: DroidDet: An effective and robust method for detecting Android malware using static analysis combined with the rotation forest model.

This paper presents a novel Non-Negative Matrix Factorization (NMF) approach tailored specifically for Collaborative Filtering (CF) in recommender systems, addressing the critical challenge of extreme sparsity in the rating matrix. Unlike existing NMF models, which are primarily geared towards computer vision tasks, this work introduces a single-element-based NMF model designed for practical implementation in the industry. The core innovation lies in the non-negative update process, which is optimized for individual features rather than the entire feature matrix. By incorporating Tikhonov regularization with non-negative single-element-based update rules, we propose the Regularized Single-Element-Based NMF (RSNMF) model. This model is uniquely suited for CF problems where non-negativity is a key constraint. Extensive experiments conducted on large-scale industrial datasets demonstrate that RSNMF achieves high accuracy with reduced computational complexity, making it a superior choice for CF in recommender systems.

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.

Plant diseases are one of the major factors that can negatively impact crop yields. Therefore, we want to create an automated system that can identify plant diseases as easily as possible. Our research goal is to help crop growers who might not be experts in plant pathology be able to identify diseases as soon as they occur in order to minimize the spread of disease and the damage to the crops. We propose a very light-weight convolutional neural network architecture that is trained to classify images of corn leaves into one of the four categories: common rust disease, northern leaf blight disease, gray leaf spot disease, or healthy. The dataset used is the PlantVillage dataset taken from plantvillage.org. The dataset consists of 8506 images of corn leaves from 4 categories. We evaluated our model using 5-fold cross-validation, and we achieves an accuracy of 97.09 % on average. The result of our model is slightly worse than the performance of other well-known CNNs architectures, but the complexity of our architecture is much lower, making it more suitable for real-time inferences on resource-constrained devices such as smartphones.

The threat that attacks cyberspace is known as malware. In order to infect the technologic devices that are attacked, malware needs to evade the different antivirus systems. To avoid detection, an obfuscation technique must be applied so malware is updated and ready to be performed. No obstant, the technique implementation presents difficulties in terms of its required ability, evasion tests and infection functionality that turn outs to be a problem to keep malware updated. Therefore, a procedure is proposed that allows applying AVFUCKER or DSPLIT techniques. The purpose is to optimize the required technical means, reduce the antivirus analysis and malware functionality check times.

Advance in information and communication technologies, force us to keep most of the information electronically, consequently, the security of information has become a fundamental issue. The traditional intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. However, normal operation often produces traffic that matches likely "attack signature", resulting in false alarms. One main drawback is the inability of detecting new attacks which do not have known signatures. In this paper particle swarm optimization (PSO) is used to implement a feature selection, and support vector machine (SVMs) with the one-versus-rest method serve as a fitness function of PSO for classification problems from the literature. Experimental result shows that our method allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. Our method simplifies features effectively and obtains a higher classification accuracy compared to other methods

This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and runtime API calls. Static code analysis approaches face challenges due to obfuscated code and adversarial perturbations. Behavioral data collected during execution is more difficult to obfuscate but recent research has shown successful attacks against API call based malware classifiers. We investigate control flow based characterization of a program execution to build robust deep learning malware classifiers. HeNet consists of a low-level behavior model and a toplevel ensemble model. The low-level model is a per-application behavior model, trained via transfer learning on a time-series of images generated from control flow trace of an execution. We use Intel R © Processor Trace enabled processor for low overhead execution tracing and design a lightweight image conversion and segmentation of the control flow trace. The top-level ensemble model aggregates the behavior classification of all the trace segments and detects an attack. The use of hardware trace adds portability to our system and the use of deep learning eliminates the manual effort of feature engineering. We evaluate HeNet against real-world exploitations of PDF readers. HeNet achieves 100% accuracy and 0% false positive on test set, and higher classification accuracy compared to classical machine learning algorithms.

An intrusion Detection System (IDS) provides alarts against intrusion attacks where a traditional firewall fails. Machine learning algorithms aim to detect anomalies using supervised and unsupervised approaches. Features selection techniques identify important features and remove irrelevant and redundant attributes to reduce the dimensionality of feature space. This paper presents a features selection framework for efficient network anomaly detection using different machine learning classifiers. The framework applies different strategies by using filter and wrapper features selection methodologies. The aim of this framework is to select the minimum number of features that achieve the highest accuracy. UNSW-NB15 dataset is used in the experimental results to evaluate the proposed framework. The results show that by using 18 features from one of the filter ranking methods and applying J48 as a classifier, an accuracy of 88% is achieved.

Botnets are networks formed with a number of machines infected by malware called bots. Detection of these malicious networks is becoming a major concern as they pose a serious threat to the network security. Most of the research on bot detection is based on particular botnet characteristics which fail to detect other types of botnets and bots. Furthermore, there are very few bot detection methods that considered real-life class-imbalanced dataset. A dataset is class-imbalanced if there are significantly more instances in one class than the other classes. In this paper, we develop three generic features to detect different types of bots regardless of their botnet characteristics. We develop five classification models based on those features to classify bots from a large, real-life, class-imbalanced network dataset. Results show that our methodology can detect bots more accurately than the existing methods. Experimental results also demonstrate that the developed methodology can successfully detect bots when the proportion of bots to normal activity is very small. We also provide a performance comparison of our methodology with a recent study on bot detection in a real-life, large, imbalanced dataset.

This paper presents a classification approach based on Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves, so the classification approach presented in this research depends on plants leave. Leaves are different from each other by characteristics such as the shape, color, texture and the margin. The used dataset for this experiments is a database of different plant species with total of only 340 leaf images, was downloaded from UCI- Machine Learning Repository. It was used for both training and testing datasets with 10-fold cross-validation. Experimental results showed that LDA achieved classification accuracy of (92.65%) against the RF that achieved accuracy of (88.82%) with combination of shape, first order texture, Gray Level Co-occurrence Matrix (GLCM), HSV color moments, and vein features.

With the rapidly growing development of cellular networks and powerful smartphones, botnets have invaded the mobile domain. Social media, like Twitter, Facebook, and YouTube have created a new communication channel for attackers. Recently, bot masters started to exploit social media for different malicious activity, such as sending spam, recruitment of new bots, and botnet command and control. In this paper we propose a detection technique for social mediabased mobile botnets using Twitter. The proposed method combines the correlation between tweeting and user activity, such as clicks or taps, and an Artificial Immune System detector, to detect tweets caused by bots and differentiate them from tweets generated by user or by user-approved applications. This detector creates a signature of the tweet and compares it with a dynamically updated signature library of bot behavior signatures. The proposed system has been fully implemented on Android platform and tested under several sets of generated tweets. The test results show that the proposed method has a very high accuracy in detecting bot tweets with about 95% detection ratio.

The phenomenal growth of the Android platform in the past few years has made it a lucrative target of malicious application (app) developers. There are numerous instances of malware apps that send premium rate SMS messages, track users' private data, or apps that, even if not characterized as malware, conduct questionable actions affecting the user's privacy or costing them money. In this paper, we investigate the feasibility of using both the permissions an app requests, the category of the app, and what permissions are requested by other apps in the same category to better inform users whether the risks of installing an app is commensurate with its expected benefit. Existing approaches consider only the risks of the permissions requested by an app and ignore both the benefits and what permissions are requested by other apps, thus having a limited effect. We propose several risk signals that and evaluate them using two datasets, one consists of 158,062 Android apps from the Android Market, and another consists of 121 malicious apps. We demonstrate the effectiveness of our proposal through extensive data analysis.