Anomaly Detection in UASN Localization Based on Time Series Analysis and Fuzzy Logic

[EN] Underwater acoustic sensor network (UASN) offers a promising solution for exploring underwater resources remotely. For getting a better understanding of sensed data, accurate localization is essential. As the UASN acoustic channel is open and the environment is hostile, the risk of malicious activities is very high, particularly in time-critical military applications. Since the location estimation with false data ends up in wrong positioning, it is necessary to identify and ignore such data to ensure data integrity. Therefore, in this paper, we propose a novel anomaly detection system for UASN localization. To minimize computational power and storage, we designed separate anomaly detection schemes for sensor nodes and anchor nodes. We propose an auto-regressive prediction-based scheme for detecting anomalies at sensor nodes. For anchor nodes, a fuzzy inference system is designed to identify the presence of anomalous behavior. The detection schemes are implemented at every node for enabling identification of multiple and duplicate anomalies at its origin. We simulated the network, modeled anomalies and analyzed the performance of detection schemes at anchor nodes and sensor nodes. The results indicate that anomaly detection systems offer an acceptable accuracy with high true positive rate and F-Score.Das, AP.; Thampi, SM.; Lloret, J. (2020). Anomaly Detection in UASN Localization Based on Time Series Analysis and Fuzzy Logic. Mobile Networks and Applications (Online). 25(1):55-67. https://doi.org/10.1007/s11036-018-1192-y556725

Security, trust and cooperation in wireless sensor networks

Wireless sensor networks are a promising technology for many real-world applications such as critical infrastructure monitoring, scientific data gathering, smart buildings, etc.. However, given the typically unattended and potentially unsecured operation environment, there has been an increased number of security threats to sensor networks. In addition, sensor networks have very constrained resources, such as limited energy, memory, computational power, and communication bandwidth. These unique challenges call for new security mechanisms and algorithms. In this dissertation, we propose novel algorithms and models to address some important and challenging security problems in wireless sensor networks. The first part of the dissertation focuses on data trust in sensor networks. Since sensor networks are mainly deployed to monitor events and report data, the quality of received data must be ensured in order to make meaningful inferences from sensor data. We first study a false data injection attack in the distributed state estimation problem and propose a distributed Bayesian detection algorithm, which could maintain correct estimation results when less than one half of the sensors are compromised. To deal with the situation where more than one half of the sensors may be compromised, we introduce a special class of sensor nodes called \textit{trusted cores}. We then design a secure distributed trust aggregation algorithm that can utilize the trusted cores to improve network robustness. We show that as long as there exist some paths that can connect each regular node to one of these trusted cores, the network can not be subverted by attackers. The second part of the dissertation focuses on sensor network monitoring and anomaly detection. A sensor network may suffer from system failures due to loss of links and nodes, or malicious intrusions. Therefore, it is critical to continuously monitor the overall state of the network and locate performance anomalies. The network monitoring and probe selection problem is formulated as a budgeted coverage problem and a Markov decision process. Efficient probing strategies are designed to achieve a flexible tradeoff between inference accuracy and probing overhead. Based on the probing results on traffic measurements, anomaly detection can be conducted. To capture the highly dynamic network traffic, we develop a detection scheme based on multi-scale analysis of the traffic using wavelet transforms and hidden Markov models. The performance of the probing strategy and of the detection scheme are extensively evaluated in malicious scenarios using the NS-2 network simulator. Lastly, to better understand the role of trust in sensor networks, a game theoretic model is formulated to mathematically analyze the relation between trust and cooperation. Given the trust relations, the interactions among nodes are modeled as a network game on a trust-weighted graph. We then propose an efficient heuristic method that explores network heterogeneity to improve Nash equilibrium efficiency

A Robot-Sensor Network Security Architecture for Monitoring Applications

This paper presents SNSR (Sensor Network Security using Robots), a novel, open, and flexible architecture that improves security in static sensor networks by benefiting from robot-sensor network cooperation. In SNSR, the robot performs sensor node authentication and radio-based localization (enabling centralized topology computation and route establishment) and directly interacts with nodes to send them configurations or receive status and anomaly reports without intermediaries. SNSR operation is divided into stages set in a feedback iterative structure, which enables repeating the execution of stages to adapt to changes, respond to attacks, or detect and correct errors. By exploiting the robot capabilities, SNSR provides high security levels and adaptability without requiring complex mechanisms. This paper presents SNSR, analyzes its security against common attacks, and experimentally validates its performance

A SURVEY OF IMPLEMENTATION OF OPPORTUNISTIC SPECTRUM ACCESS ATTACK WITH ITS PREVENTIVE SENSING PROTOCOLS IN COGNITIVE RADIO NETWORKS

Recently, the expansive growth of wireless services, regulated by governmental agencies assigning spectrum to licensed users, has led to a shortage of radio spectrum. Since the FCC (Federal Communications Commissions) approved unlicensed users to access the unused channels of the reserved spectrum, new research areas seeped in, to develop Cognitive Radio Networks (CRN), in order to improve spectrum efficiency and to exploit this feature by enabling secondary users to gain from the spectrum in an opportunistic manner via optimally distributed traffic demands over the spectrum, so as to reduce the risk for monetary loss, from the unused channels. However, Cognitive Radio Networks become vulnerable to various classes of threats that decrease the bandwidth and spectrum usage efficiency. Hence, this survey deals with defining and demonstrating framework of one such attack called the Primary User Emulation Attack and suggests preventive Sensing Protocols to counteract the same. It presents a scenario of the attack and its prevention using Network Simulator-2 for the attack performances and gives an outlook on the various techniques defined to curb the anomaly

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

A critical analysis of mobility management related issues of wireless sensor networks in cyber physical systems

Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems; its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area