Question: Integration of sensor data for enhanced semantic segmentation in urban environments.

This paper explores the application of graph cuts for the approximate minimization of energy functions commonly encountered in early vision tasks, focusing on scenarios where the energy function's smoothness term is defined over pairs of pixels. We introduce two algorithms designed to find a local minimum even when substantial label changes are permitted. The first label alteration we consider is an α-β swap, involving the exchange of labels between two arbitrary sets of pixels bearing labels α and β. Our proposed algorithm identifies a labeling where no swap move can result in a decrease in energy. The second label change we examine is an α-expansion, which assigns an arbitrary set of pixels the label α. This second algorithm, contingent on the smoothness term being metric, produces a labeling where no expansion move can lower the energy, and the solution is guaranteed to be within a known factor of the global minimum. We validate the efficacy of our approach through experiments on image restoration, stereo, and motion analysis.

In the realm of early vision, a multitude of problems necessitate the estimation of spatially varying quantities from noisy data. These quantities, such as intensity or disparity, exhibit piecewise smoothness, smoothly varying in most regions but experiencing abrupt changes at object boundaries. Each pixel p ∈ P is assigned a label from a set L, where the labels could represent disparities in stereo or motion analysis, or intensities in image restoration tasks. The objective is to determine a labeling f that assigns each pixel p ∈ P a label fp ∈ L, ensuring that f is both piecewise smooth and compatible with the observed data. This challenge is naturally formulated as an energy minimization problem, where the goal is to find the labeling f that minimizes the energy E(f) = Esmooth(f) + Edata(f). Here, Esmooth quantifies the degree to which f deviates from being piecewise smooth, while Edata gauges the discrepancy between f and the observed data. A plethora of energy function formulations have been proposed in the literature, with Edata typically defined as

Vehicle localization and autonomous navigation consist of precisely positioning a vehicle on road by the use of different kinds of sensors. This paper presents a vehicle localization method by integrating a stereoscopic system, a laser range finder (LRF) and a global localization sensor GPS. For more accurate LRF-based vehicle motion estimation, an outlier-rejection invariant closest point method (ICP) is proposed to reduce the matching ambiguities of scan alignment. The fusion approach starts by a sensor selection step that is applied to validate the coherence of the observations from different sensors. Then the information provided by the validated sensors is fused with an unscented information filter. To demonstrate its performance, the proposed multisensor localization method is tested with real data and evaluated by RTK-GPS data as ground truth. The fusion approach also facilitates the incorporation of more sensors if needed.

In this paper, we present an infrastructure-free mapping and localization framework for rail vehicles using only a lidar sensor. Our method is designed to handle the pathological environment found in modern underground tunnels: narrow, parallel, and relatively smooth concrete walls with very little infrastructure to break up the empty spaces in the tunnel. By using an RQE-based, point-cloud alignment approach, we are able to implement a sliding-window algorithm, used for both mapping and localization. We demonstrate the proposed method with datasets gathered on a subway train travelling at high speeds (up to 70 km/h) in an underground tunnel for a total of 20 km across 6 runs. Our method is capable of mapping the tunnel with less than 0.6% error over the total length of the generated map. It is capable of continuously localizing, relative to the generated map, to within 10 cm in stations and at crossovers, and 1.8 m in pathological sections of tunnel. This method improves rail-based localization in a tunnel, which can be used to increase capacity on existing railways and for automated trains.

Fusion of different sensor modalities has proven very effective in numerous remote sensing applications. However, in order to benefit from fusion, advanced feature extraction mechanisms that rely on domain expertise are typically required. In this paper we present an automated feature extraction scheme based on deep learning. The feature extraction is unsupervised and hierarchical. Furthermore, computational efficiency (often a challenge for deep learning methods) is a primary goal in order to make certain that the method can be applied in large remote sensing datasets. Promising classification results show the applicability of the approach for both reducing the gap between naive feature extraction and methods relying on domain expertise, as well as further improving the performance of the latter in two challenging datasets.

This letter presents a two-step method for urban main road extraction from high-resolution remotely sensed imagery by integrating spectral-spatial classification and shape features. In the first step, spectral-spatial classification segments the imagery into two classes, i.e., the road class and the nonroad class, using path openings and closings. The local homogeneity of the gray values obtained by local Geary's C is then fused with the road class. In the second step, the road class is refined by using shape features. The experimental results indicated that the proposed method was able to achieve a comparatively good performance in urban main road extraction.

The evolution of the Web of documents into a Web of services and data has resulted in an increased availability of data from almost any domain. For example, general domain knowledge bases such as DBpedia or Wikidata, or domain specific Web sources like the Oxford Art archive, allow for accessing knowledge about a wide variety of entities including people, organizations, or art paintings. However, these data sources publish data in different ways, and they may be equipped with different search capabilities, e.g., SPARQL endpoints or REST services, thus requiring data integration techniques that provide a unified view of the published data. We devise a semantic data integration approach named FuhSen that exploits keyword and structured search capabilities of Web data sources and generates on-demand knowledge graphs merging data collected from available Web sources. Resulting knowledge graphs model semantics or meaning of merged data in terms of entities that satisfy keyword queries, and relationships among those entities. FuhSen relies on both RDF to semantically describe the collected entities, and on semantic similarity measures to decide on relatedness among entities that should be merged. We empirically evaluate the results of FuhSen data integration techniques on data from the DBpedia knowledge base. The experimental results suggest that FuhSen data integration techniques accurately integrate similar entities semantically into knowledge graphs.

Detection of traversable road regions, positive and negative obstacles, and water hazards is a fundamental task for autonomous driving vehicles, especially in off-road environment. This paper proposes an efficient method, called Lidar-histogram. It can be used to integrate the detection of traversable road regions, obstacles and water hazards into one single framework. The weak assumption of the Lidar-histogram is that a decent-sized area in front of the vehicle is flat. The Lidar-histogram is derived from an efficient organized map of Lidar point cloud, called Lidar-imagery, to index, describe and store Lidar data. The organized point-cloud map can be easily obtained by indexing the original unordered 3D point cloud to a Lidar-specific 2D coordinate system. In the Lidar-histogram representation, the 3D traversable road plane in front of vehicle can be projected as a straight line segment, and the positive and negative obstacles are projected above and below the line segment, respectively. In this way, the problem of detecting traversable road and obstacles is converted into a simple linear classification task in 2D space. Experiments have been conducted in different kinds of off-road and urban scenes, and we have obtained very promising results.

Application of high level fusion approaches demonstrate a sequence of significant advantages in multi sensor data fusion and automotive safety fusion systems are no exception to this. High level fusion can be applied to automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper two specific high level data fusion approaches are described including a brief architectural and algorithmic presentation. These approaches differ mainly in their data association part: (a) track level fusion approach solves it with the point to point association with emphasis on object continuity and multidimensional assignment, and (b) grid based fusion approach that proposes a generic way to model the environment and to perform sensor data fusion. The test case for these approaches is a multi sensor equipped PReVENT/ProFusion2 truck demonstrator vehicle.

Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return.In this paper, we propose VeTrack, a smartphone-only system that tracks the vehicle's location in real time using the phone's inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel "shadow" tracing method to accurately estimate the vehicle's trajectories despite arbitrary phone/vehicle poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle's real time location with almost negligible latency, with error of 2-4 parking spaces at 80-percentile.

Nowadays, GPS-based car navigation systems mainly use speech and aerial views of simplified road maps to guide drivers to their destination. However, drivers often experience difficulties in linking the simple 2D aerial map with the visual impression that they get from the real environment, which is inherently ground-level based. Therefore, supplying realistically textured 3D city models at ground-level proves very useful for pre-visualizing an upcoming traffic situation. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the latter will more easily understand the required maneuver. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. We present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed which could allow for pre-visualization of any conceivable traffic situation by car navigation modules.

This paper presents the Radar and IR sensor fusion method for objection detection. The infrared camera parameter calibration with Levenberg-Marquardt (LM) optimization method is proposed based on the Radar ranging data represented by Cartesian coordinate compared with 6 fusion methods. The proposed method firstly performs the estimation of the intrinsic parameter matrix of infrared camera with some optical trick. Then the method searches the extrinsic parameters using the generative approach. The initial angle and translation of the extrinsic parameters are optimized by the LM method with the geometrical cost function. In the experiments, the performance of proposed method outperforms by a maximum 13 times the performance of the other baseline methods on the averaged Euclidian distance error. In future work, the angular noise of the Radar information will be improved and the proposed method will provide the effective proposals for the deep neural network.


Question: Fusion of sensor data for semantic segmentation in urban environments.

This paper introduces an efficient 3D object recognition system designed to identify multiple objects concurrently within cluttered and partially occluded environments. The system's core functionality revolves around matching surfaces through the innovative use of the spin-image representation, a sophisticated shape descriptor that enables the comparison of surface meshes. A compression technique for spin-images is proposed, significantly enhancing the system's performance in multiple object recognition. This enhancement is substantiated through empirical results, which showcase the successful simultaneous recognition of multiple objects from a database of 20 models. Moreover, the robustness of the recognition process in challenging environments, characterized by clutter and occlusion, is demonstrated through a comprehensive analysis of 100 recognition trials on diverse scenes. The research, conducted at Carnegie Mellon University and funded by the US Department of Energy under contract DE-AC21-92MC29104, contributes a novel approach to addressing the complexities of object recognition in real-world settings.

**Question**: A Comparative Study of Feature Selection Methods for High-Dimensional Data

**Documents**: A survey of feature selection and feature extraction methods in machine learning
Feature selection and feature extraction are two important preprocessing steps for machine learning and data mining. Feature selection aims at selecting a subset of the original features, while feature extraction aims at transforming the original features into a new set of features. Both methods are used to reduce the dimensionality of the data, improve the interpretability of the model, and reduce the computational cost. This paper provides a comprehensive survey of feature selection and feature extraction methods. We first introduce the basic concepts and the taxonomy of feature selection and feature extraction methods. Then, we review the state-of-the-art methods in each category, including filter methods, wrapper methods, embedded methods, and hybrid methods for feature selection, and principal component analysis, linear discriminant analysis, independent component analysis, and kernel methods for feature extraction. We also discuss the advantages and disadvantages of each method, and provide guidelines for choosing appropriate methods for different applications. Finally, we highlight some open issues and future research directions in feature selection and feature extraction.

**Similar Documents**: This paper offers an extensive review of feature selection and feature extraction techniques within the realm of machine learning and data mining, two critical preprocessing steps aimed at optimizing data for model training and interpretation. Feature selection focuses on identifying a subset of the most relevant features from the original dataset, whereas feature extraction involves transforming the original features into a new set that better represents the underlying data structure. Both strategies are employed to mitigate dimensionality issues, enhance model interpretability, and lower

We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGBD images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.

Calibration of the LiDAR sensor with RGB camera finds its usage in many application fields from enhancing image classification to the environment perception and mapping. This paper presents a pipeline for mutual pose and orientation estimation of the mentioned sensors using a coarse to fine approach. Previously published methods use multiple views of a known chessboard marker for computing the calibration parameters, or they are limited to the calibration of the sensors with a small mutual displacement only. Our approach presents a novel 3D marker for coarse calibration which can be robustly detected in both the camera image and the LiDAR scan. It also requires only a single pair of camera-LiDAR frames for estimating large sensors displacement. Consequent refinement step searches for more accurate calibration in small subspace of calibration parameters. The paper also presents a novel way for evaluation of the calibration precision using projection error.

Pedestrians are important participants in urban traffic environments, and thus act as an interesting category of objects for autonomous cars. Automatic pedestrian detection is an essential task for protecting pedestrians from collision. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance.

In this paper, we present an infrastructure-free mapping and localization framework for rail vehicles using only a lidar sensor. Our method is designed to handle the pathological environment found in modern underground tunnels: narrow, parallel, and relatively smooth concrete walls with very little infrastructure to break up the empty spaces in the tunnel. By using an RQE-based, point-cloud alignment approach, we are able to implement a sliding-window algorithm, used for both mapping and localization. We demonstrate the proposed method with datasets gathered on a subway train travelling at high speeds (up to 70 km/h) in an underground tunnel for a total of 20 km across 6 runs. Our method is capable of mapping the tunnel with less than 0.6% error over the total length of the generated map. It is capable of continuously localizing, relative to the generated map, to within 10 cm in stations and at crossovers, and 1.8 m in pathological sections of tunnel. This method improves rail-based localization in a tunnel, which can be used to increase capacity on existing railways and for automated trains.

For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and optical conditions. Recently, convolutional neural networks (CNNs) have shown outstanding performance on this task. Although many deep neural network structures and techniques have been applied to improve the accuracy, few have paid attention to better differentiating the easily confused classes. In this paper, we propose TreeSegNet which adopts an adaptive network to increase the classification rate at the pixelwise level. Specifically, based on the infrastructure of DeepUNet, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively according to the confusion matrix and the proposed TreeCutting algorithm. By transporting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model. In experiments on the ISPRS 2D semantic labeling Potsdam dataset, the results obtained by TreeSegNet are better than those of other  Corresponding author. Tel.: +86 18911938368. E-mail address: ilydouble@gmail.com TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation 2 published state-of-the-art methods. Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.

The emergence of Mobile Mapping Systems (MMS) has set a marked paradigm in the photogrammetric and mapping community that has not only facilitated comprehensive 3D mapping of different environments but has also paved way for new aspects of applied research in this direction. Out of the many essential blocks that make these MMS a viable tool for mapping, the positioning and orientation module is considered to be a crucial yet an expensive component. The integration of such a module with mapping sensors has allowed for the extensive implementation of such systems to provide high-quality maps. However, while such systems do not lack in system robustness and performance in general, the deployment of these systems is restricted to applications and environments where a consistent availability of GNSS signals is assured. Extending these MMS to GNSS-denied areas, such as indoor environments, is therefore quite challenging and necessitates the development of an alternative module that can act as a viable substitute to GNSS/INS for system operation without having to resort to an exhaustive modification of the same to function in GNSS-denied locations. In this research, such a case has been considered for the implementation of an indoor MMS using an Unmanned Ground Vehicle (UGV) and a 3D laser scanner for the task of generating high density maps of GNSS-denied indoor areas. To mitigate the absence of GNSS data, this paper proposes a Pseudo-GNSS/INS module integrated framework which utilizes probabilistic Simultaneous Localization and Mapping (SLAM) techniques to estimate the platform pose and heading from 3D laser scanner data. This proposed framework has been implemented based on three major notions: (i) using geometric methods for sparse point cloud extraction to carry out real-time SLAM, (ii) generating position data and geo-referencing signals from these real-time SLAM pose estimates, and (iii) carrying out the entire operation through use of a single 3D mapping sensor. The final geo-referenced point cloud can then be generated through post-processing by the Iterative Closest Projected Point (ICPP) registration technique which also diminishes the effect of sensor measurement noise. The implementation, performance and results of the proposed MMS framework for an indoor mapping system have been presented in this paper that demonstrate the ability of this Pseudo-GNSS/INS framework to operate flexibly in GNSS-denied areas.

In sensing applications where multiple sensors observe the same scene, fusing sensor outputs can provide improved results. However, if some of the sensors are providing lower quality outputs, e.g. when one or more sensors has a poor signal-tonoise ratio (SNR) and therefore provides very noisy data, the fused results can be degraded. In this work, a multi-sensor conflict measure is proposed which estimates multi-sensor conflict by representing each sensor output as interval-valued information and examines the sensor output overlaps on all possible n-tuple sensor combinations. The conflict is based on the sizes of the intervals and how many sensors output values lie in these intervals. In this work, conflict is defined in terms of how little the output from multiple sensors overlap. That is, high degrees of overlap mean low sensor conflict, while low degrees of overlap mean high conflict. This work is a preliminary step towards a robust conflict and sensor fusion framework. In addition, a sensor fusion algorithm is proposed based on a weighted sum of sensor outputs, where the weights for each sensor diminish as the conflict measure increases. The proposed methods can be utilized to (1) assess a measure of multi-sensor conflict, and (2) improve sensor output fusion by lessening weighting for sensors with high conflict. Using this measure, a simulated example is given to explain the mechanics of calculating the conflict measure, and stereo camera 3D outputs are analyzed and fused. In the stereo camera case, the sensor output is corrupted by additive impulse noise, DC offset, and Gaussian noise. Impulse noise is common in sensors due to intermittent interference, a DC offset a sensor bias or registration error, and Gaussian noise represents a sensor output with low SNR. The results show that sensor output fusion based on the conflict measure shows improved accuracy over a simple averaging fusion strategy.

Accurate maps of the static environment are essential for many advanced driver-assistance systems. A new method for the fast computation of occupancy grid maps with laser range-finders and radar sensors is proposed. The approach utilizes the Graphics Processing Unit to overcome the limitations of classical occupancy grid computation in automotive environments. It is possible to generate highly accurate grid maps in just a few milliseconds without the loss of sensor precision. Moreover, in the case of a lower resolution radar sensor it is shown that it is suitable to apply super-resolution algorithms to achieve the accuracy of a higher resolution laser-scanner. Finally, a novel histogram based approach for road boundary detection with lidar and radar sensors is presented.

To increase autonomous ground vehicle (AGV) safety and efficiency on outdoor terrains the vehicle's control system should have settings for individual terrain surfaces. A first step in such a terrain-dependent control system is classification of the surface upon which the AGV is traversing. This paper considers vision-based terrain surface classification for the path directly in front of the vehicle (≪ 1 m). Most visionbased terrain classification has focused on terrain traversability and not on terrain surface classification. The few approaches to classifying traversable terrain surfaces, with the exception of the use of infrared cameras to classify mud, have relied on stand-alone cameras that are designed for daytime use and are not expected to perform well in the dark. In contrast, this research uses a laser stripe-based structured light sensor, which uses a laser in conjunction with a camera, and hence can work at night. Also, unlike most previous results, the classification here does not rely on color since color changes with illumination and weather, and certain terrains have multiple colors (e.g., sand may be red or white). Instead, it relies only on spatial relationships, specifically spatial frequency response and texture, which captures spatial relationships between different gray levels. Terrain surface classification using each of these features separately is conducted by using a probabilistic neural network. Experimental results based on classifying four outdoor terrains demonstrate the effectiveness of the proposed methods.

This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.


Question: Fusion of sensor data for semantic segmentation in urban environments.

This paper introduces a cutting-edge perception system designed for enhancing ground robot mobility, specifically focusing on 3D object detection, classification, and tracking using LIDAR technology. This system was successfully implemented on our autonomous ground vehicle, MuCAR-3, facilitating safe navigation in environments mimicking urban traffic and off-road convoy scenarios. The system's efficiency is attributed to its innovative integration of 2D and 3D data processing techniques. Object segmentation from point clouds is rapidly achieved in a 2D occupancy grid, while object classification is performed directly on raw 3D point clouds. To expedite feature extraction, we propose utilizing statistics of local point cloud properties, encapsulated in histograms over point features. Unlike conventional 3D point cloud classification methods that often struggle with real-time operation, our system's unique combination of techniques enables it to function in real-time at a frame-rate of 0.1s, demonstrating its superiority in dynamic urban environments.

We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 92% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We implement and experiment with several variations within the AdaBoost family of algorithms. We observe that our results are robust and stable over all the various tests and algorithmic variations. We also investigate features and values that are most critical in distinguishing between the classes. This insight is important in extending the results from one geographic region to another.

This paper aims to highlight vision related tasks centered around “car”, which has been largely neglected by vision community in comparison to other objects. We show that there are still many interesting car-related problems and applications, which are not yet well explored and researched. To facilitate future car-related research, in this paper we present our on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes. Importantly, the dataset is constructed with a cross-modality nature, containing a surveillance-nature set and a web-nature set. We further demonstrate a few important applications exploiting the dataset, namely car model classification, car model verification, and attribute prediction. We also discuss specific challenges of the car-related problems and other potential applications that worth further investigations. The latest dataset can be downloaded at http://mmlab.ie.cuhk.edu.hk/ datasets/comp_cars/index.html.

For autonomous vehicles and advanced driver assistance systems, information on the actual state of the environment is fundamental for localization and mapping tasks. Localization benefits from multiple observations of the same location at different times as these may provide important information on static and mobile objects. For efficient mapping, the environment may be explored in parallel. For these purposes, multiple observations represented by grid maps have to be aligned into one mutual frame. This paper addresses the problem of group-wise grid map registration using an image processing approach. For registration, a rotational-invariant descriptor is proposed in order to provide the correspondences of points of interest in radar-based occupancy grid maps. As pairwise registration of multiple grid maps suffers from bias, this paper proposes a graph-based approach for robust registration of multiple grid maps. This will facilitate highly accurate range sensor maps for the aforementioned purposes. Large-scale experiments show the benefit of the proposed methods and compare it to state-of-the-art algorithms on radar measurements.

Driving an automobile autonomously on rural roads requires knowledge about the geometry of the road. Furthermore, knowledge about the meaning of each lane of the road is needed in order to decide which lane should be taken and if the vehicle can do a lane change. This paper addresses the problem of extracting additional information about lanes. The information is extracted from the types of road-markings. The type of lane border markings is estimated in order to find out if a lane change is allowed. Arrows, which are painted on the road, are extracted and classified in order to determine the meaning of a lane such as a turn off lane.

Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g . road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

Updating road markings is one of the routine tasks of transportation agencies. Compared with traditional road inventory mapping techniques, vehicle-borne mobile light detection and ranging (LiDAR) systems can undertake the job safely and efficiently. However, current hurdles include software and computing challenges when handling huge volumes of highly dense and irregularly distributed 3-D mobile LiDAR point clouds. This paper presents the development and implementation aspects of an automated object extraction strategy for rapid and accurate road marking inventory. The proposed road marking extraction method is based on 2-D georeferenced feature (GRF) images, which are interpolated from 3-D road surface points through a modified inverse distance weighted (IDW) interpolation. Weighted neighboring difference histogram (WNDH)-based dynamic thresholding and multiscale tensor voting (MSTV) are proposed to segment and extract road markings from the noisy corrupted GRF images. The results obtained using 3-D point clouds acquired by a RIEGL VMX-450 mobile LiDAR system in a subtropical urban environment are encouraging.

Many driver assistant and safety systems depend on an accurate environmental model containing the positions of stationary objects, the states of dynamic objects and information about valid driving corridors. Therefore, a robust differentiation between moving and stationary objects is required. This is challenging for laser scanners, because these sensors are not able to measure the velocity of objects directly. Therefore, an advanced occupancy grid approach, the online map, is introduced, which enables the robust separation of moving and stationary objects. The online map is used for the robust detection of the road boundaries for the determination of driving corridors in urban and highway scenarios. An algorithm for the detection of arbitrary moving objects using the online map is proposed.

Weather-dependent road conditions are a major factor in many automobile incidents; computer vision algorithms for automatic classification of road conditions can thus be of great benefit. This paper presents a system for classification of road conditions using still-frames taken from an uncalibrated dashboard camera. The problem is challenging due to variability in camera placement, road layout, weather and illumination conditions. The system uses a prior distribution of road pixel locations learned from training data then fuses normalized luminance and texture features probabilistically to categorize the segmented road surface. We attain an accuracy of 80% for binary classification (bare vs. snow/ice-covered) and 68% for 3 classes (dry vs. wet vs. snow/ice-covered) on a challenging dataset, suggesting that a useful system may be viable.


Question: Insights Gained from Past SSL/TLS Assaults - A Concise Timeline of Exploits and Vulnerabilities

The Galois/Counter Mode (GCM) of Operation: An Examination of Its Security and Applications

This paper delves into the Galois/Counter Mode (GCM) of operation, a widely used symmetric-key algorithm for encrypting and authenticating data. GCM is an efficient mode of operation for block ciphers, offering both confidentiality and integrity protection. It employs a counter mode for encryption and a Galois field multiplication for authentication. This combination results in high performance and security, making GCM suitable for various applications, including network protocols and storage systems.

GCM's design is centered around simplicity and speed. It uses a single block cipher for both encryption and authentication, eliminating the need for separate algorithms. This simplifies implementation and reduces computational overhead. The counter mode allows for parallel processing, enhancing performance in hardware and software environments. Additionally, GCM supports variable-length data, making it flexible for different use cases.

Security is a paramount concern in GCM. The authentication mechanism, based on Galois field multiplication, ensures data integrity and authenticity. It detects any unauthorized modifications to the ciphertext, preventing tampering. However, GCM's security is contingent upon proper implementation and usage. Misconfigurations, such as using the same nonce with the same key for multiple messages, can lead to vulnerabilities. Implementers must adhere to strict guidelines to ensure GCM's effectiveness.

GCM's versatility has led to its widespread adoption. It is an integral part of various cryptographic standards and protocols, including TLS, IPsec, and IEEE 802.1AE (MACsec). GCM's efficiency and security make it a preferred choice for applications requiring high-speed encryption and authentication, such as real-time communication and data storage.

Despite its strengths, GCM has faced scrutiny and analysis from the cryptographic community. Researchers have explored its potential weaknesses and have proposed improvements and alternatives. The study of GCM's security properties and practical implications is ongoing, contributing to the advancement of cryptographic techniques.

In conclusion, the Galois/Counter Mode (GCM) of operation is a significant development in symmetric-key cryptography. Its combination of encryption and authentication capabilities, along with its performance and flexibility, make it a popular choice for securing data. However, its proper implementation and usage are critical to maintaining its security. Ongoing research and analysis will continue to refine GCM and guide its application in various cryptographic contexts.

Cryptographic applications often run more than one independent algorithm such as encryption and authentication. This fact provides a high level of parallelism which can be exploited by software and converted into instruction level parallelism to improve overall performance on modern super-scalar processors. We present fast and efficient methods of computing such pairs of functions on IA processors using a method called " function stitching ". Instead of computing pairs of functions sequentially as is done today in applications/libraries, we replace the function calls by a single call to a composite function that implements both algorithms. The execution time of this composite function can be made significantly shorter than the sums of the execution times for the individual functions and, in many cases, close to the execution time of the slower function. Function stitching is best done at a very fine grain, interleaving the code for the individual algorithms at an instruction-level granularity. This results in excellent utilization of the execution resources in the processor core with a single thread. We show how stitching pairs of functions together in a fine-grained manner results in excellent performance on IA processors. Currently, applications perform the functions sequentially. We demonstrate performance gains of 1.4X-1.9X with stitching over the best sequential function performance. We show performance results achieved by this method on the Intel ® processors based on the Westmere architecture.

An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences:Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intended recipient. Only he can decipher the message, since only he knows the corresponding decryption key.
A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret prime numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d = 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.

Many authentication schemes depend on secret passwords. Unfortunately, the length and randomness of user-chosen passwords remain xed over time. In contrast, hardware improvements constantly give attackers increasing computational power. As a result, password schemes such as the traditional UNIX user-authentication system are failing with time. This paper discusses ways of building systems in which password security keeps up with hardware speeds. We formalize the properties desirable in a good password system, and show that the computational cost of any secure password scheme must increase as hardware improves. We present two algorithms with adaptable cost|eksblow sh, a block cipher with a purposefully expensive key schedule, and bcrypt, a related hash function. Failing a major breakthrough in complexity theory, these algorithms should allow password-based systems to adapt to hardware improvements and remain secure well into the future.

Password-Authenticated Key Exchange (PAKE) protocols allow two parties that only share a password to establish a shared key in a way that is immune to offline attacks. Asymmetric PAKE (aPAKE) strengthens this notion for the more common client-server setting where the server stores a mapping of the password and security is required even upon server compromise, that is, the only allowed attack in this case is an (inevitable) offline exhaustive dictionary attack against individual user passwords. Unfortunately, current aPAKE protocols (that dispense with the use of servers’ public keys) allow for pre-computation attacks that lead to the instantaneous compromise of user passwords upon server compromise, thus forgoing much of the intended aPAKE security. Indeed, these protocols use – in essential ways – deterministic password mappings or use random “salt” transmitted in the clear from servers to users, and thus are vulnerable to pre-computation attacks. We initiate the study of Strong aPAKE protocols that are secure as aPAKE’s but are also secure against pre-computation attacks. We formalize this notion in the Universally Composable (UC) settings and present two modular constructions using an Oblivious PRF as a main tool. The first builds a Strong aPAKE from any aPAKE (which in turn can be constructed from any PAKE [23]) while the second builds a Strong aPAKE from any authenticated key-exchange protocol secure against reverse impersonation (a.k.a. KCI). Using the latter transformation, we show a practical instantiation of a UC-secure Strong aPAKE in the Random Oracle model. The protocol (“OPAQUE”) consists of 2 messages (3 with mutual authentication), requires 3 and 4 exponentiations for server and client, respectively (2 to 4 of which can be fixed-base depending on optimizations), provides forward secrecy, is PKI-free, supports user-side hash iterations, has a built-in facility for password-based storage and retrieval of secrets and credentials, and accommodates a user-transparent server-side threshold implementation.

An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences: (1) Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intented recipient. Only he can decipher the message, since only he knows the corresponding decryption key. (2) A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret primer numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d ≡ 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.

With the advent of side-channel analysis, implementations of mathematically secure ciphers face a new threat: by exploiting the physical characteristics of a device, adversaries are able to break algorithms such as AES or Triple-DES (3DES), for which no efficient analytical or brute-force attacks exist. In this paper, we demonstrate practical, noninvasive side-channel attacks on the Mifare DESFire MF3ICD40 contactless smartcard, a 3DES-based alternative to the cryptanalytically weak Mifare Classic [9, 25]. We detail on how to recover the complete 112-bit secret key of the employed 3DES algorithm, using non-invasive power analysis and template attacks. Our methods can be put into practice at a low cost with standard equipment, thus posing a severe threat to many real-world applications that employ the DESFire MF3ICD40 smartcard.

Software vulnerabilities represent a major cause of cybersecurity problems. The National Vulnerability Database (NVD) is a public data source that maintains standardized information about reported software vulnerabilities. Since its inception in 1997, NVD has published information about more than 43,000 software vulnerabilities affecting more than 17,000 software applications. This information is potentially valuable in understanding trends and patterns in software vulnerabilities, so that one can better manage the security of computer systems that are pestered by the ubiquitous software security flaws. In particular, one would like to be able to predict the likelihood that a piece of software contains a yet-to-be-discovered vulnerability, which must be taken into account in security management due to the increasing trend in zero-day attacks. We conducted an empirical study on applying data-mining techniques on NVD data with the objective of predicting the time to next vulnerability for a given software application. We experimented with various features constructed using the information available in NVD, and applied various machine learning algorithms to examine the predictive power of the data. Our results show that the data in NVD generally have poor prediction capability, with the exception of a few vendors and software applications. By doing a large number of experiments and observing the data, we suggest several reasons for why the NVD data have not produced a reasonable prediction model for time to next vulnerability with our current approach.


Question: Insights Gained From Past SSL/TLS Assaults - A Concise Timeline Of Exploits And Vulnerabilities

This paper outlines a lattice-based assault on the Digital Signature Algorithm (DSA) in scenarios where the algorithm is repeatedly employed to authenticate numerous messages, denoted as mi. The attack assumes that a significant fraction of the bits from each corresponding ephemeral key, yi, can be deduced through alternative methodologies. By exploiting lattice reduction techniques, the attacker can potentially reconstruct the private key used in DSA, thus compromising the security of the digital signatures. This approach highlights a vulnerability in the DSA mechanism when not all bits of the ephemeral keys are adequately protected, and it serves as a critical lesson in the context of SSL/TLS attacks, emphasizing the importance of safeguarding ephemeral keys to prevent lattice-based attacks from succeeding. The history of SSL/TLS weaknesses, including this particular DSA vulnerability, underscores the ongoing battle against cryptographic attacks and the necessity for robust cryptographic practices to ensure secure communications.

This paper describes the Elliptic Curve Cryptography algorithm and its suitability for smart cards.

We present a method for detecting exploitations of vulnerabilities in privileged programs by monitoring their execution using audit trials, where the monitoring is with respect to specifications of the security-relevant behavior of the programs. Our work is motivated by the intrusion detection paradigm, but is a n attempt to avoid a d hoc approaches to codifying misuse behavior. Our approach as based on the observation that although privileged programs can be exploited (due to errors) to cause security compromise in systems because of the privileges accorded to them, the intended behavior of privileged programs is, of course, limited a n d benign. The key, then i s to specify the intended behavior (Le., the program policy) and to detect any action by privileged program that is outside the intended behavior a n d that imperils security. We describe a program policy specification language, which is based on simple predicate logic and regular expressions. I n addition, we present specifications of privileged programs in Unix, and Q prototype execution monitor for analyzing audit trails with respect to these specifications. The program policies are surprisingly concise and clear, a n d an addition, capable of detecting exploitations of known vulnerabilities in these programs. Although our work has been motivated by the known vulnerabilities in Unix, we believe that by tightly restricting the behavior of all privileged programs, exploitations of unknown vulnerabilities can be detected. A s Q check on the specifications, work is in progress on verifying them with respect to a n abstract security policy. *This work is funded in part by the National Security Agency University Research Program under Contract No. DOD-MDA904-93-C4083 and by ARPA under Contract No. USNN00014-94-1-0065.

Once attackers have injected code into a victim program's address space, or found a memory disclosure vulnerability, all sensitive data and code inside that address space are subject to thefts or manipulation. Unfortunately, this broad type of attack is hard to prevent, even if software developers wish to cooperate, mostly because the conventional memory protection only works at process level and previously proposed in-process memory isolation methods are not practical for wide adoption. We propose shreds, a set of OS-backed programming primitives that addresses developers' currently unmet needs for fine-grained, convenient, and efficient protection of sensitive memory content against in-process adversaries. A shred can be viewed as a flexibly defined segment of a thread execution (hence the name). Each shred is associated with a protected memory pool, which is accessible only to code running in the shred. Unlike previous works, shreds offer in-process private memory without relying on separate page tables, nested paging, or even modified hardware. Plus, shreds provide the essential data flow and control flow guarantees for running sensitive code. We have built the compiler toolchain and the OS module that together enable shreds on Linux. We demonstrated the usage of shreds and evaluated their performance using 5 non-trivial open source software, including OpenSSH and Lighttpd. The results show that shreds are fairly easy to use and incur low runtime overhead (4.67%).

New Cryptographic protocols which take full advantage of the unique properties of public key cryptosystems are now evolving. Several protocols for public key distribution and for digital signatures are briefly compared with each other and with the conventional alternative.

Recent work by Krawczyk [12] and Menezes [16] has highlighted the importance of understanding well the guarantees and limitations of formal security models when using them to prove the security of protocols. In this paper we focus on security models for authenticated key exchange (AKE) protocols. We observe that there are several classes of attacks on AKE protocols that lie outside the scope of the Canetti-Krawczyk model. Some of these additional attacks have already been considered by Krawczyk [12]. In an attempt to bring these attacks within the scope of the security model we extend the Canetti-Krawczyk model for AKE security by providing significantly greater powers to the adversary. Our contribution is a more compact, integrated, and comprehensive formulation of the security model. We then introduce a new AKE protocol called NAXOS and prove that it is secure against these stronger

We propose a novel trap-based architecture for detecting passive, “silent”, attackers who are eavesdropping on enterprise networks. Motivated by the increasing number of incidents where attackers sniff the local network for interesting information, such as credit card numbers, account credentials, and passwords, we introduce a methodology for building a trap-based network that is designed to maximize the realism of bait-laced traffic. Our proposal relies on a “record, modify, replay” paradigm that can be easily adapted to different networked environments. The primary contributions of our architecture are the ease of automatically injecting large amounts of believable bait, and the integration of different detection mechanisms in the back-end. We demonstrate our methodology in a prototype platform that uses our decoy injection API to dynamically create and dispense network traps on a subset of our campus wireless network. Our network traps consist of several types of monitored passwords, authentication cookies, credit cards, and documents containing beacons to alarm when opened. The efficacy of our decoys against a model attack program is also discussed, along with results obtained from experiments in the field. In addition, we present a user study that demonstrates the believability of our decoy traffic, and finally, we provide experimental results to show that our solution causes only negligible interference to ordinary users.

Sergei Arnautov, Bohdan Trach, Franz Gregor, Thomas Knauth, Andre Martin, Christian Priebe, Joshua Lind, Divya Muthukumaran, Dan O’Keeffe, Mark L Stillwell, David Goltzsche, David Eyers, Rüdiger Kapitza, Peter Pietzuch, and Christof Fetzer Fakultät Informatik, TU Dresden, christof.fetzer@tu-dresden.de Dept. of Computing, Imperial College London, prp@imperial.ac.uk Informatik, TU Braunschweig, rrkapitz@ibr.cs.tu-bs.de Dept. of Computer Science, University of Otago, dme@cs.otago.ac.nz

Exploitation of buffer overflow vulnerabilities constitutes a significant portion of security attacks in computer systems. One of the most common types of buffer overflow attacks is the hijacking of the program counter by overwriting function return addresses in the process’ stack so as to redirect the program’s control flow to some malicious code injected into the process’ memory. Previous solutions to this problem are based either on hardware or the compiler. The former requires special hardware while the latter requires the source code of the software. In this paper we introduce the use of a T ransparent RUntime Shadow Stack (TRUSS) to protect against function return address modification. Our proposed scheme is built on top of DynamoRIO, a dynamic binary rewriting framework. DynamoRIO is implemented on both Windows and Linux. Hence, our scheme is able to protect applications on both operating systems. We have successfully tested our implementation on the SPECINT 2000 benchmark programs on both Windows and Linux, John Wilander’s “Dynamic testbed for twenty buffer overflow attacks” as well as Microsoft Access, Powerpoint and Word 2002. This paper will discuss the implementation details of our scheme as well as provide a performance evaluation. The latter shows that TRUSS is able to operate with an average overhead of about 20% to 50% which we believe is acceptable.

Cache side-channel attacks have been extensively studied on x86 architectures, but much less so on ARM processors. The technical challenges to conduct side-channel attacks on ARM, presumably, stem from the poorly documented ARM cache implementations, such as cache coherence protocols and cache flush operations, and also the lack of understanding of how different cache implementations will affect side-channel attacks. This paper presents a systematic exploration of vectors for flush-reload attacks on ARM processors. flush-reload attacks are among the most well-known cache side-channel attacks on x86. It has been shown in previous work that they are capable of exfiltrating sensitive information with high fidelity. We demonstrate in this work a novel construction of flush-reload side channels on last-level caches of ARM processors, which, particularly, exploits return-oriented programming techniques to reload instructions. We also demonstrate several attacks on Android OS (e.g., detecting hardware events and tracing software execution paths) to highlight the implications of such attacks for Android devices.

Detecting encrypted traffic is increasingly important for deep packet inspection (DPI) to improve the performance of intrusion detection systems. We propose a machine learning approach with several randomness tests to achieve high accuracy detection of encrypted traffic while requiring low overhead incurred by the detection procedure. To demonstrate how effective the proposed approach is, the performance of four classification methods (Näıve Bayesian, Support Vector Machine, CART and AdaBoost) are explored. Our recommendation is to use CART which is not only capable of achieving an accuracy of 99.9% but also up to about 2.9 times more efficient than the second best candidate (Näıve Bayesian).


Question: Insights Gained From Past SSL/TLS Assaults - A Concise Timeline Of Vulnerabilities And Intrusions

Vulnerabilities and Countermeasures in Cryptographic Systems: Timing Attacks on Key Operations

Careful measurement of the time taken for private key operations can expose vulnerabilities in various cryptographic systems, including Diffie-Hellman, RSA, DSS, and others. This timing analysis can enable attackers to uncover fixed Diffie-Hellman exponents, factor RSA keys, and compromise other encryption mechanisms. The attack is not only computationally affordable but can often be executed with merely known ciphertext. Various real-world systems are susceptible, ranging from cryptographic tokens to network-based cryptosystems and other applications where attackers can obtain reasonably precise timing measurements.

Among the systems at risk, some require significant revisions to protect against timing attacks. For instance, the paper outlines techniques for safeguarding RSA and Diffie-Hellman systems. It highlights that certain cryptosystems might need substantial redesigns, and new protocols and algorithms must be developed with built-in defenses against timing attacks. This underscores the critical importance of understanding and mitigating timing vulnerabilities in cryptographic implementations to ensure the security of sensitive data and communications.

In the last decade, it has become aparent that embedded systems are integral parts of our every day lives. The wireless nature of many embedded applications as well as their omnipresence has made the need for security and privacy preserving mechanisms particularly important. Thus, as FPGAs become integral parts of embedded systems, it is imperative to consider their security as a whole. This contribution provides a state-of-the-art description of security issues on FPGAs, both from the system and implementation perspectives. We discuss the advantages of reconfigurable hardware for cryptographic applications, show potential security problems of FPGAs, and provide a list of open research problems. Moreover, we summarize both public and symmetric-key algorithm implementations on FPGAs.

Bit coin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bit coin grew to comprise billions of dollars of economic value despite only cursory analysis of the system's design. Since then a growing literature has identified hidden-but-important properties of the system, discovered attacks, proposed promising alternatives, and singled out difficult future challenges. Meanwhile a large and vibrant open-source community has proposed and deployed numerous modifications and extensions. We provide the first systematic exposition Bit coin and the many related crypto currencies or 'altcoins.' Drawing from a scattered body of knowledge, we identify three key components of Bit coin's design that can be decoupled. This enables a more insightful analysis of Bit coin's properties and future stability. We map the design space for numerous proposed modifications, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools. We survey anonymity issues in Bit coin and provide an evaluation framework for analyzing a variety of privacy-enhancing proposals. Finally we provide new insights on what we term disinter mediation protocols, which absolve the need for trusted intermediaries in an interesting set of applications. We identify three general disinter mediation strategies and provide a detailed comparison.

Software vulnerabilities represent a major cause of cybersecurity problems. The National Vulnerability Database (NVD) is a public data source that maintains standardized information about reported software vulnerabilities. Since its inception in 1997, NVD has published information about more than 43,000 software vulnerabilities affecting more than 17,000 software applications. This information is potentially valuable in understanding trends and patterns in software vulnerabilities, so that one can better manage the security of computer systems that are pestered by the ubiquitous software security flaws. In particular, one would like to be able to predict the likelihood that a piece of software contains a yet-to-be-discovered vulnerability, which must be taken into account in security management due to the increasing trend in zero-day attacks. We conducted an empirical study on applying data-mining techniques on NVD data with the objective of predicting the time to next vulnerability for a given software application. We experimented with various features constructed using the information available in NVD, and applied various machine learning algorithms to examine the predictive power of the data. Our results show that the data in NVD generally have poor prediction capability, with the exception of a few vendors and software applications. By doing a large number of experiments and observing the data, we suggest several reasons for why the NVD data have not produced a reasonable prediction model for time to next vulnerability with our current approach.

Security issues are becoming more and more important to acti vities of individuals, organizations and the society in our modern ne tworked computerized world. In this chapter we survey a few optimization framewor ks for problems related to security of various networked system such as the int erne or the power grid system.

We analyze the concrete security and key sizes of theoretically sound lattice-based encryption schemes based on the “learning with errors” (LWE) problem. Our main contributions are: (1) a new lattice attack on LWE that combines basis reduction with an enumeration algorithm admitting a time/success tradeoff, which performs better than the simple distinguishing attack considered in prior analyses; (2) concrete parameters and security estimates for an LWE-based cryptosystem that is more compact and efficient than the well-known schemes from the literature. Our new key sizes are up to 10 times smaller than prior examples, while providing even stronger concrete security levels.

The application of elliptic curves to the field of cryptography has been relatively recent. It has opened up a wealth of possibilities in terms of security, encryption, and real-world applications. In particular, we are interested in public-key cryptosystems that use the elliptic curve discrete logarithm problem to establish security. The objective of this thesis is to assemble the most important facts and findings into a broad, unified overview of this field. To illustrate certain points, we also discuss a sample implementation of the elliptic curve analogue of the El

Nowadays, it is hard to find a popular Web site with a registration form that is not protected by an automated human proof test which displays a sequence of characters in an image, and requests the user to enter the sequence into an input field. This security mechanism is based on the Turing Test—one of the oldest concepts in Artificial Intelligence—and it is most often called Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). This kind of test has been conceived to prevent the automated access to an important Web resource, for example, a Web mail service or a Social Network. There are currently hundreds of these tests, which are served millions of times a day, thus involving a huge amount of human work. On the other side, a number of these tests have been broken, that is, automated programs designed by researchers, hackers, and spammers have been able to automatically serve the correct answer. In this chapter, we present the history and the concept of CAPTCHAs, along with their applications and a wide review of their instantiations. We also discuss their evaluation, both from the user and the security perspectives, including usability, attacks, and countermeasures. We expect this chapter provides to the reader a good overview of this interesting field. CES IN COMPUTERS, VOL. 83 109 Copyright © 2011 Elsevier Inc. 65-2458/DOI: 10.1016/B978-0-12-385510-7.00003-5 All rights reserved. 110 J.M. GOMEZ HIDALGO AND G. ALVAREZ MARAÑON 1. I ntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 1 .1. T he Turing Test and the Origin of CAPTCHAs . . . . . . . . . . . . . . . 111 2. M otivation and Applications . . . . . . . . . . . . . . . . . . . . . . . . 116 2 .1. G eneral Description of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . 116 2 .2. D esirable Properties of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . 117 2 .3. I m plementation and Deployment . . . . . . . . . . . . . . . . . . . . . . 119 2 .4. A pplications and the Rise of the Robots . . . . . . . . . . . . . . . . . . . 121 3. T ypes of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 3 .1. O CR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 3 .2. I m age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 3 .3. A udio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 3 .4. C ognitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 4. E valuation of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . . . . . 146 4 .1. E fficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 4 .2. A ccessibility Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 4 .3. P ractical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 5. S ecurity and Attacks on CAPTCHAs . . . . . . . . . . . . . . . . . . . . 156 5 .1. A ttacks on CAPTCHAs . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 5 .2. S ecurity Requirements on CAPTCHAs . . . . . . . . . . . . . . . . . . . 169 6. A lternatives to CAPTCHAs . . . . . . . . . . . . . . . . . . . . . . . . . 171 7. C onclusions and Future Trends . . . . . . . . . . . . . . . . . . . . . . . 173 R eferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

Recently, the mobile industry has experienced an extreme increment in number of its users. The GSM network with the greatest worldwide number of users succumbs to several security vulnerabilities. Although some of its security problems are addressed in its upper generations, there are still many operators using 2G systems. This paper briefly presents the most important security flaws of the GSM network and its transport channels. It also provides some practical solutions to improve the security of currently available 2G systems.

Editor: Peter Anderson Rochester Institute of Technology 1 Lomb Memorial Dr. Rochester, NY 14623 pga@cs.rit.edu Each technique receives similar coverage, including a description of its new and prerequisite vocabulary, its model, the bug assumptlons that underlie its effectiveness, typical applications, examples (almost all based on standard IRS tax forms), caveats and limitations, automation and tools for the technique, a self-evaluation quiz, and exercises. Common threads in the presentation show the role


Question: Insights Gained from Past SSL/TLS Assaults - A Concise Timeline of Exploits and Vulnerabilities

This paper outlines the design and characteristics of the RC5 encryption algorithm, a high-speed symmetric block cipher that is well-suited for both hardware and software implementations. A distinctive feature of RC5 is its extensive use of data-dependent rotations, which adds a layer of complexity to the encryption process. The algorithm is adaptable, with a customizable word size, variable number of rounds, and a secret key of variable length. Both the encryption and decryption processes in RC5 are notably straightforward, offering a balance between security and efficiency.

**Question**: The Role of Renewable Energy in Achieving Energy Security

**Documents**: An Analysis of the Impact of Solar Power on National Energy Security
This study explores the effects of solar power on national energy security, focusing on the reduction of dependency on fossil fuels, the diversification of energy sources, and the enhancement of energy independence. Solar energy's potential to mitigate geopolitical risks associated with traditional energy sources and its role in promoting sustainable economic growth are also discussed.

**Similar Documents**: This research delves into the significance of solar power in bolstering national energy security, examining its influence on decreasing reliance on fossil fuels, broadening the energy mix, and strengthening self-sufficiency in energy. The analysis highlights solar energy's capability to alleviate geopolitical tensions linked to conventional energy resources and its contribution to fostering a sustainable economy. The study underscores the multifaceted benefits of solar power in achieving a more secure and sustainable energy future.

We describe a new technique for finding potential buffer overrun vulnerabilities in security-critical C code. The key to success is to use static analysis: we formulate detection of buffer overruns as an integer range analysis problem. One major advantage of static analysis is that security bugs can be eliminated before code is deployed. We have implemented our design and used our prototype to find new remotely-exploitable vulnerabilities in a large, widely deployed software package. An earlier hand audit missed

Rubén Prieto-Díaz Commonwealth Information Security Center James Madison University prietodiaz@cisat.jmu.edu

Due to increasing security awareness of password from the public and little attention on the characteristics of real-life passwords, it is thus natural to understand the current state of characteristics of real-life passwords, and to explore how password characteristics change over time and how earlier password practice is understood in current context. In this work, we attempt to present an in-depth and comprehensive understanding of user practice in real-life passwords, and to see whether the previous observations can be confirmed or reversed, based on large-scale measurements rather than anecdotal experiences or user surveys. Specifically, we measure password characteristics on over 6 million passwords, in terms of password length, password composition, and password selection. We then make informed comparisons of the findings between our investigation and previously reported results. Our general findings include: (1) average password length is at least 12% longer than previous results, and 85% of our passwords have the length between 8 and 11 characters;

There have been many incidents of prefix hijacking in the Internet. The hijacking AS can blackhole the hijacked traffic. Alternatively, it can transparently intercept the hijacked traffic by forwarding it onto the owner. This paper presents a study of such prefix hijacking and interception with the following contributions: (1). We present a methodology for prefix interception, (2). We estimate the fraction of traffic to any prefix that can be hijacked and intercepted in the Internet today, (3). The interception methodology is implemented and used to intercept real traffic to our prefix, (4). We conduct a detailed study to detect ongoing prefix interception.
 We find that: Our hijacking estimates are in line with the impact of past hijacking incidents and show that ASes higher up in the routing hierarchy can hijack a significant amount of traffic to any prefix, including popular prefixes. A less apparent result is that the same holds for prefix interception too. Further, our implementation shows that intercepting traffic to a prefix in the Internet is almost as simple as hijacking it. Finally, while we fail to detect ongoing prefix interception, the detection exercise highlights some of the challenges posed by the prefix interception problem.

During the last several years the advancements in technology made it possible for small sensor nodes to communicate wirelessly with the rest of the Internet. With this achievement the question of securing such IP-enabled Wireless Sensor Networks (IP-WSNs) emerged and has been an important research topic since. In this thesis we discuss our implementation of TLS and DTLS protocols using a pre-shared key cipher suite (TLS PSK WITH AES 128 CCM 8) for the Contiki operating system. Apart from simply adding a new protocol to the set of protocols supported by the Contiki OS, this project allows us to evaluate how suitable the transport-layer security and pre-shared key management schemes are for IP-WSNs.

This paper presents a systematic solution to the persistent problem of buffer overflow attacks. Buffer overflow attacks gained notoriety in 1988 as part of the Morris Worm incident on the Internet. While it is fairly simple to fix individual buffer overflow vulnerabilities, buffer overflow attacks continue to this day. Hundreds of attacks have been discovered, and while most of the obvious vulnerabilities have now been patched, more sophisticated buffer overflow attacks continue to emerge. We describe StackGuard: a simple compiler technique that virtually eliminates buffer overflow vulnerabilities with only modest performance penalties. Privileged programs that are recompiled with the StackGuard compiler extension no longer yield control to the attacker, but rather enter a fail-safe state. These programs require no source code changes at all, and are binary-compatible with existing operating systems and libraries. We describe the compiler technique (a simple patch to gcc), as well as a set of variations on the technique that tradeoff between penetration resistance and performance. We present experimental results of both the penetration resistance and the performance impact of this technique. This research is partially supported by DARPA contracts F3060296-1-0331 and F30602-96-1-0302. Ryerson Polytechnic University

JavaScript is used by web developers to enhance the interactivity of their sites, offload work to the users' browsers and improve their sites' responsiveness and user-friendliness, making web pages feel and behave like traditional desktop applications. An important feature of JavaScript, is the ability to combine multiple libraries from local and remote sources into the same page, under the same namespace. While this enables the creation of more advanced web applications, it also allows for a malicious JavaScript provider to steal data from other scripts and from the page itself. Today, when developers include remote JavaScript libraries, they trust that the remote providers will not abuse the power bestowed upon them.
 In this paper, we report on a large-scale crawl of more than three million pages of the top 10,000 Alexa sites, and identify the trust relationships of these sites with their library providers. We show the evolution of JavaScript inclusions over time and develop a set of metrics in order to assess the maintenance-quality of each JavaScript provider, showing that in some cases, top Internet sites trust remote providers that could be successfully compromised by determined attackers and subsequently serve malicious JavaScript. In this process, we identify four, previously unknown, types of vulnerabilities that attackers could use to attack popular web sites. Lastly, we review some proposed ways of protecting a web application from malicious remote scripts and show that some of them may not be as effective as previously thought.

The Bitcoin cryptocurrency records its transactions in a public log called the blockchain. Its security rests critically on the distributed protocol that maintains the blockchain, run by participants called miners. Conventional wisdom asserts that the mining protocol is incentive-compatible and secure against colluding minority groups, that is, it incentivizes miners to follow the protocol as prescribed.
 We show that the Bitcoin mining protocol is not incentive-compatible. We present an attack with which colluding miners' revenue is larger than their fair share. The attack can have significant consequences for Bitcoin: Rational miners will prefer to join the attackers, and the colluding group will increase in size until it becomes a majority. At this point, the Bitcoin system ceases to be a decentralized currency.
 Unless certain assumptions are made, selfish mining may be feasible for any coalition size of colluding miners. We propose a practical modification to the Bitcoin protocol that protects Bitcoin in the general case. It prohibits selfish mining by a coalition that command less than 1/4 of the resources. This threshold is lower than the wrongly assumed 1/2 bound, but better than the current reality where a coalition of any size can compromise the system.