Robust Distributed Estimation of Wireless Sensor Networks Under Adversarial Attacks

IEEE Transactions on Vehicular Technology, vol. 73, pp. 7102–7113

Abstract

This paper focuses on the parameter estimation problem in wireless sensor networks (WSNs) under adversarial attacks, considering the complexities of sensing and communication in challenging environments. In order to mitigate the impact of these attacks on the network, we propose a novel AP-DLMS algorithm with adaptive threshold attack detection and malicious punishment mechanism. The adaptive threshold is constructed using the observation matrix and network topology to detect the location of malicious attacks, while the standard reference estimation is designed to obtain the estimated deviation of each node. To mitigate the impact of data tampering on network performance, we introduce the honesty factor and punishment factor to combine the weights of normal nodes and malicious nodes respectively. Additionally, we propose a new probabilistic random attack model. Simulations are conducted to investigate the influence of key parameters in the adaptive threshold on the performance of the proposed AP-DLMS algorithm, and the mean square performance of the algorithm is analyzed under various attack models. The results demonstrate that the proposed algorithm exhibits strong robustness in adversarial networks, and the proposed attack model effectively demonstrates the impact of attacks.

Authors 8

1. Chaoyang Chen

   Hunan University of Science and Technology · Chinese Academy of Sciences · Shenzhen Institutes of Advanced Technology

   Affiliation as printed

   School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China

   Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

2. Dingrong Tan

   Hunan University of Science and Technology

   Affiliation as printed

   School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China

3. Pei Li

   Hunan University of Science and Technology

   Affiliation as printed

   School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China

4. Juan Chen

   Hunan University of Science and Technology

   Affiliation as printed

   School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China

5. Guan Gui

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

6. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, U.K

   Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, United Kingdom

7. Haris Gacanin Aachen Institute for Communication Technologies and Embedded Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany

8. Fumiyuki Adachi

   Tohoku University

   Affiliation as printed

   International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai, Japan