Journal article Open Access

Countering Intelligent Dependent Malicious Nodes in Target Detection Wireless Sensor Networks

Althunibat, Saud; Antonopoulos, Angelos; Kartsakli, Elli; Granelli, Fabrizio; Verikoukis, Christos

Target detection wireless sensor networks (WSNs), where binary decisions are transmitted to declare the presence or absence of a given target, are expected to have a fundamental role in the Internet of Things era. However, their simplicity makes these networks very susceptible to malicious attacks, while the problem is aggravated in the presence of intelligent malicious nodes that adapt their strategy depending on the behavior of other nodes in the network. In this paper, first, we analytically demonstrate that dependent and independent malicious nodes have the same impact on the overall performance of target detection WSNs in terms of detection and false alarm rates. Then, taking into account that dependent malicious users cannot be detected by conventional algorithms, we introduce an effective algorithm that detects malicious nodes in the network regardless of their type and number. Finally, theoretical and simulation results are provided to show the effects of dependent malicious nodes and analyze the performance of the proposed algorithm compared with the existing state-of-the-art works.

Grant numbers : This work is funded by CellFive (TEC2014-60130-P) and by the Catalan Government 2014-SGR-1551.© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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