A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.
To overcome the deficiencies of the generate-and-test approach a different approach is outlined in @cite_4 . Several signals each having a different function are employed in order to detect a specific misbehavior in sensor wireless networks. Unfortunately, no performance analysis was presented and the properties of these signals were not evaluated with respect to their misuse.
@cite_4
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In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper we investigate the use of an Artificial Immune System (AIS) to detect node misbehavior in a mobile ad-hoc network using DSR. The system is inspired by the natural immune system of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. We describe the first step of our design; it employs negative selection, an algorithm used by the natural immune system. We define how we map the natural immune system concepts such as self, antigen and antibody to a mobile ad-hoc network, and give the resulting algorithm for misbehavior detection. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters impact the results. Further steps will extend the design by using an analogy to the innate system, danger signals, costimulation and memory cells. The proliferation of mobile computing devices has enabled immense opportunities for everyday users. At the same time, however, this has opened up new, and perhaps more severe, possibilities for attacks. In this paper, we explore a novel generation of mobile malware that exploits the rich variety of sensors available on current mobile devices. Two properties distinguish the proposed malware from the existing state-of-the-art. First, in addition to the misuse of the various traditional services available on modern mobile devices, this malware can be used for the purpose of targeted context-aware attacks. Second, this malware can be commanded and controlled over context-aware, out-of-band channels as opposed to a centralized infrastructure. These communication channels can be used to quickly reach out to a large number of infected devices, while offering a high degree of undetectability. In particular, unlike traditional network-based communication, the proposed sensing-enabled channels cannot be detected by monitoring the cellular or wireless communication networks. To demonstrate the feasibility of our proposed attack, we present different flavors of command and control channels based on acoustic, visual, magnetic and vibrational signaling. We further build and test a proof-of-concept Android application implementing many such channels. 1 Introduction.- 1.1 Distributed Detection Systems.- 1.2 Outline of the Book.- 2 Elements of Detection Theory.- 2.1 Introduction.- 2.2 Bayesian Detection Theory.- 2.3 Minimax Detection.- 2.4 Neyman-Pearson Test.- 2.5 Sequential Detection.- 2.6 Constant False Alarm Rate (CFAR) Detection.- 2.7 Locally Optimum Detection.- 3 Distributed Bayesian Detection: Parallel Fusion Network.- 3.1 Introduction.- 3.2 Distributed Detection Without Fusion.- 3.3 Design of Fusion Rules.- 3.4 Detection with Parallel Fusion Network.- 4 Distributed Bayesian Detection: Other Network Topologies.- 4.1 Introduction.- 4.2 The Serial Network.- 4.3 Tree Networks.- 4.4 Detection Networks with Feedback.- 4.5 Generalized Formulation for Detection Networks.- 5 Distributed Detection with False Alarm Rate Constraints.- 5.1 Introduction.- 5.2 Distributed Neyman-Pearson Detection.- 5.3 Distributed CFAR Detection.- 5.4 Distributed Detection of Weak Signals.- 6 Distributed Sequential Detection.- 6.1 Introduction.- 6.2 Sequential Test Performed at the Sensors.- 6.3 Sequential Test Performed at the Fusion Center.- 7 Information Theory and Distributed Hypothesis Testing.- 7.1 Introduction.- 7.2 Distributed Detection Based on Information Theoretic Criterion.- 7.3 Multiterminal Detection with Data Compression.- Selected Bibliography. The authors propose an audio events detection system tailored to surveillance applications.The method has been tested on a huge and challenging data set, made publicly available.The performance analysis has been done for low SNR values and under various conditions.A comparative analysis with other methods from the literature has been performed. In this paper we propose a novel method for the detection of audio events for surveillance applications. The method is based on the bag of words approach, adapted to deal with the specific issues of audio surveillance: the need to recognize both short and long sounds, the presence of a significant noise level and of superimposed background sounds of intensity comparable to the audio events to be detected. In order to test the proposed method in complex, realistic scenarios, we have built a large, publicly available dataset of audio events. The dataset has allowed us to evaluate the robustness of our method with respect to varying levels of the Signal-to-Noise Ratio; the experimentation has confirmed its applicability under real world conditions, and has shown a significant performance improvement with respect to other methods from the literature. In active and assisted living environments, a major service that can be provided is the automated assessment of elderly people’s well-being. Therefore, activity recognition is required to detect what types of help disabled persons need to support them in their daily life activities. Unfortunately, it is still a difficult task to estimate the size of the required window for online sensor data streams to recognize a specific activity, especially when new sensor events are recorded. This paper proposes a windowing algorithm, which presents promising results to recognize complex human activities for multi-resident homes. The approach is based on the analysis of the sensor data to identify the best fitting sensors that should be considered in a specified window. Moreover, the second part of this paper proposes a set of different statistical spatio-temporal features to recognize human activities. In order to check the overall performance, this approach is tested using the CASAS data set and artificially generated laboratory data using our HBMS simulator. The results show high performance based on different evaluation metrics compared to other approaches. We believe that the proposed windowing approach provides a good approximation of the required window size in order to robustly detect human activities in comparison to other windowing approaches. Wireless sensor networks are built upon a shared medium that makes it easy for adversaries to conduct radio interference, or jamming, attacks that effectively cause a denial of service of either transmission or reception functionalities. These attacks can easily be accomplished by an adversary by either bypassing MAC-layer protocols or emitting a radio signal targeted at jamming a particular channel. In this article we survey different jamming attacks that may be employed against a sensor network. In order to cope with the problem of jamming, we discuss a two-phase strategy involving the diagnosis of the attack, followed by a suitable defense strategy. We highlight the challenges associated with detecting jamming. To cope with jamming, we propose two different but complementary approaches. One approach is to simply retreat from the interferer which may be accomplished by either spectral evasion (channel surfing) or spatial evasion (spatial retreats). The second approach aims to compete more actively with the interferer by adjusting resources, such as power levels and communication coding, to achieve communication in the presence of the jammer. In this paper, we propose a method for separating speech signals when there are more signals than sensors. Several methods have already been proposed for solving the underdetermined problem, and some of these utilize the sparseness of speech signals. These methods employ binary masks to extract the signals, and therefore, their extracted signals contain loud musical noise. To overcome this problem, we propose combining a sparseness approach and independent component analysis (ICA). First, using sparseness, we estimate the time points when only one source is active. Then, we remove this single source from the observations and apply ICA to the remaining mixtures. Experimental results show that our proposed sparseness and ICA (SPICA) method can separate signals with little distortion even in reverberant conditions of T sub R =130 and 200 ms. A robust filter-based approach is proposed for wireless sensor networks for detecting contours of a signal distribution over a 2-dimensional region. The motivation for contour detection is derived from applications where the spatial distribution of a signal (such as temperature, soil moisture level, etc.) is to be determined over a large region with minimum communication cost. The proposed scheme applies multi-level quantization to the sensor signal values to artificially create an edge and then applies spatial filtering for edge detection. The spatial filter is localized and is based on an adaptation of the Prewitt filter used in image processing. Appropriate mechanisms are introduced that minimizes the cost for communication required for collaboration. Simulation results are presented to show the error performance of the proposed contour detection scheme and the associated communication cost (single-hop communications with immediate neighborhood in average) in the network. A possible future application in communications is the wireless uplink transmission in sensor networks. This application is mainly characterized by sporadic transmission over a random access channel. Since each sensor has a low activity probability, the signal for Multi-User Detection (MUD) is sparse. Compressive Sensing (CS) theory introduces detectors that are able to recover sparse signals reliably. When applied to MUD, CS detectors perform a joint detection of both data and user activity. This allows for less control signaling, since information about the activity state of each user no longer need to be signaled. Additionally, CS detectors are able to reliably detect sparse signals even in under-determined systems. In the context of transmission with Code Division Multiple Access (CDMA) this property can be exploited by reducing the length of spreading sequences, which increases the symbol-rate for a given bandwidth, and making the CDMA system overloaded. In this paper we introduce the application of greedy CS detection algorithms to detect CDMA spread multi-user data. Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality-of-Service requirements including high detection probability, low false alarm rate, and bounded detection delay. Although a dense all-static network may initially meet these Quality-of-Service requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher Signal-to-Noise Ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance under a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes. A class of energy efficient schemes is proposed for detection and tracking of the boundaries of correlated spatial distributions, such as air pollution in large cities at a known threshold level, using large scale wireless sensor network. It is shown that without having the exact statistics of the signal, distributed collaborative space and time oriented filtering can be used to efficiently detect the boundary of the threshold level of a correlated spatial distribution over time. Collaborative signal processing is used to reduce the effect of noisy observations and distributed space and time domain filtering is applied for energy conservative threshold level tracking. It is shown that by using spatial and time oriented filtering, the operating mode of the wireless sensor network is shifted from communication dominant mode toward computation and sensing dominant mode, which significantly saves the in-network energy. The performance of the discussed approach for a few correlated random spatial distributions is evaluated using computer simulations. One of the key issues in practical speech processing is to achieve robust voice activity detection (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ the Gaussian assumption in the discrete Fourier transform (DFT) domain, which, however, deviates from the real observation. In this paper, we propose a class of VAD algorithms based on several statistical models. In addition to the Gaussian model, we also incorporate the complex Laplacian and Gamma probability density functions to our analysis of statistical properties. With a goodness-of-fit tests, we analyze the statistical properties of the DFT spectra of the noisy speech under various noise conditions. Based on the statistical analysis, the likelihood ratio test under the given statistical models is established for the purpose of VAD. Since the statistical characteristics of the speech signal are differently affected by the noise types and levels, to cope with the time-varying environments, our approach is aimed at finding adaptively an appropriate statistical model in an online fashion. The performance of the proposed VAD approaches in both the stationary and nonstationary noise environments is evaluated with the aid of an objective measure. Web-based vulnerabilities represent a substantial portion of the security exposures of computer networks. In order to detect known web-based attacks, misuse detection systems are equipped with a large number of signatures. Unfortunately, it is difficult to keep up with the daily disclosure of web-related vulnerabilities, and, in addition, vulnerabilities may be introduced by installation-specific web-based applications. Therefore, misuse detection systems should be complemented with anomaly detection systems. This paper presents an intrusion detection system that uses a number of different anomaly detection techniques to detect attacks against web servers and web-based applications. The system analyzes client queries that reference server-side programs and creates models for a wide-range of different features of these queries. Examples of such features are access patterns of server-side programs or values of individual parameters in their invocation. In particular, the use of application-specific characterization of the invocation parameters allows the system to perform focused analysis and produce a reduced number of false positives. The system derives automatically the parameter profiles associated with web applications (e.g., length and structure of parameters) and relationships between queries (e.g., access times and sequences) from the analyzed data. Therefore, it can be deployed in very different application environments without having to perform time-consuming tuning and configuration. US water utilities are faced with mounting operational and maintenance costs as a result of aging pipeline infrastructures. Leaks and ruptures in water supply pipelines and blockages and overflow events in sewer collectors cost millions of dollars a year, and monitoring and repairing this underground infrastructure presents a severe challenge. In this paper, we discuss how wireless sensor networks (WSNs) can increase the spatial and temporal resolution of operational data from pipeline infrastructures and thus address the challenge of near real-time monitoring and eventually control. We focus on the use of WSNs for monitoring large diameter bulk-water transmission pipelines. We outline a system, PipeNet, we have been developing for collecting hydraulic and acoustic vibration data at high sampling rates as well as algorithms for analyzing this data to detect and locate leaks. Challenges include sampling at high data rates, maintaining aggressive duty cycles, and ensuring tightly time- synchronized data collection, all under a strict power budget. We have carried out an extensive field trial with Boston Water and Sewer Commission in order to evaluate some of the critical components of PipeNet. Along with the results of this preliminary trial, we describe the results of extensive laboratory experiments which are used to evaluate our analysis and data processing solutions. Our prototype deployment has led to the development of a reusable, field-reprogrammable software infrastructure for distributed high-rate signal processing in wireless sensor networks, which we also describe. This paper studies the beneficial combination of wireless power transfer and millimeter-wave (mmWave) communications in wireless sensor networks. In particular, an mmWave wireless powered sensor network is considered, where the access point (AP) employs beamforming techniques to transfer energy to the sensors of a selected sector of the cell. The served sensors harvest and store energy from the received signal, and use it to power their uplink transmissions. We consider a random energy beamforming scheme but also propose several intelligent schemes that steer the beam to specific areas of the cell by considering the sensors’ locations. This setup is investigated from a large-scale point-of-view where spatial randomness is considered with the aid of Poisson point processes. The performance of the network is described in terms of the energy outage probability and the beam outage probability. We show that, depending on the scenario, each of the considered energy beamforming schemes can provide significant gains to the network’s performance. Finally, we study an event monitoring application of our theoretical framework, where the active sensors observe a random event in the network and the AP attempts to estimate it based on the received information; this scenario is evaluated in terms of the estimation’s mean squared error. In this paper, we investigate a binary decentralized detection problem in which a network of wireless sensors provides relevant information about the state of nature to a fusion center. Each sensor transmits its data over a multiple access channel. Upon reception of the information, the fusion center attempts to accurately reconstruct the state of nature. We consider the scenario where the sensor network is constrained by the capacity of the wireless channel over which the sensors are transmitting, and we study the structure of an optimal sensor configuration. For the problem of detecting deterministic signals in additive Gaussian noise, we show that having a set of identical binary sensors is asymptotically optimal, as the number of observations per sensor goes to infinity. Thus, the gain offered by having more sensors exceeds the benefits of getting detailed information from each sensor. A thorough analysis of the Gaussian case is presented along with some extensions to other observation distributions. This paper presents algorithms for efficiently detecting the variation of a distributed signal over space and time using large scale wireless sensor networks. The proposed algorithms use contours for estimating the spatial distribution of a signal. A contour tracking algorithm is proposed to efficiently monitor the variations of the contours with time. Use of contours reduces the communication cost by reducing the participation of sensor nodes for the monitoring tasks. The proposed schemes use multi-sensor collaboration techniques and non-uniform contour levels to reduce the error in reconstructing the signal distribution. Results from computer simulations are presented to demonstrate the performance of the proposed schemes. We develop a matched signal detection (MSD) theory for signals with an intrinsic structure described by a weighted graph. Hypothesis tests are formulated under different signal models. In the simplest scenario, we assume that the signal is deterministic with noise in a subspace spanned by a subset of eigenvectors of the graph Laplacian. The conventional matched subspace detection can be easily extended to this case. Furthermore, we study signals with certain level of smoothness. The test turns out to be a weighted energy detector, when the noise variance is negligible. More generally, we presume that the signal follows a prior distribution, which could be learnt from training data. The test statistic is then the difference of signal variations on associated graph structures, if an Ising model is adopted. Effectiveness of the MSD on graph is evaluated both by simulation and real data. We apply it to the network classification problem of Alzheimer's disease (AD) particularly. The preliminary results demonstrate that our approach is able to exploit the sub-manifold structure of the data, and therefore achieve a better performance than the traditional principle component analysis (PCA). A recent trend in the field of biometrics is ECG-based, where electrocardiogram (ECG) signals are used as input to the biometric system. Previous work has shown that ECG has a good potential, which can be used alone as a biometric parameter or in combination with some other parameters for greater accuracy, due to its main key properties. This paper presents a study on the applicability of ECG signals in securing Wireless Body Area Network (WBAN) communications. We study the permanence and the distinctiveness properties of ECG signals on 10 random patients. The Independent Component Analysis (ICA) and Fast Fourier Transform (FFT) are applied on the ECG signals obtained from MIT-BIH Normal Synus Rhythm (nsrdb) public database. The experimental results are presented, which exhibit that ECG signal can be utilized properly to achieve better security performance under the stringent constraints of WBAN sensors. Thus, it is believed that the system can naturally secure the information transmission within WBAN, where other techniques use hardware and software to achieve the same purpose. Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for sparse signals. For large wireless sensor networks, the events are relatively sparse compared with the number of sources. Because of deployment cost, the number of sensors is limited, and due to energy constraint, not all the sensors are turned on all the time. In this paper, the first contribution is to formulate the problem for sparse event detection in wireless sensor networks as a compressive sensing problem. The number of (wake-up) sensors can be greatly reduced to the similar level of the number of sparse events, which is much smaller than the total number of sources. Second, we suppose the event has the binary nature, and employ the Bayesian detection using this prior information. Finally, we analyze the performance of the compressive sensing algorithms under the Gaussian noise. From the simulation results, we show that the sampling rate can reduce to 25 without sacrificing performance. With further decreasing the sampling rate, the performance is gradually reduced until 10 of sampling rate. Our proposed detection algorithm has much better performance than the l 1 -magic algorithm proposed in the literature.