Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk (SW) tests, are proposed to detect and suppress spectrum sensing data falsification (SSDF) attacks by malicious users in cooperative spectrum sensing. First, we consider basic and statistical SSDF attacks, where the malicious users attack independently. Then we propose a new SSDF attack, which involves cooperation among malicious users by masking. In practice, the number of malicious users is unknown. Thus, it is necessary to estimate the number of malicious users, which is found using clustering and largest gap method. However, we show using Monte Carlo simulations that, these methods fail to estimate the exact number of malicious users when they cooperate. To overcome this, we propose a modified largest gap method.Comment: Accepted in Proceedings of 79th IEEE Vehicular Technology Conference-Spring (VTC-Spring), May 2014, Seoul, South Kore

Safe and Secure Wireless Power Transfer Networks: Challenges and Opportunities in RF-Based Systems

RF-based wireless power transfer networks (WPTNs) are deployed to transfer power to embedded devices over the air via RF waves. Up until now, a considerable amount of effort has been devoted by researchers to design WPTNs that maximize several objectives such as harvested power, energy outage and charging delay. However, inherent security and safety issues are generally overlooked and these need to be solved if WPTNs are to be become widespread. This article focuses on safety and security problems related WPTNs and highlight their cruciality in terms of efficient and dependable operation of RF-based WPTNs. We provide a overview of new research opportunities in this emerging domain.Comment: Removed some references, added new references, corrected typos, revised some sections (mostly I-B and III-C

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