Detecting Reinforcement Learning-Based Grey Hole Attack in Mobile Wireless Sensor Networks

Boqi GAO  Takuya MAEKAWA  Daichi AMAGATA  Takahiro HARA  

IEICE TRANSACTIONS on Communications   Vol.E103-B   No.5   pp.504-516
Publication Date: 2020/05/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2019EBP3151
Type of Manuscript: PAPER
Category: Fundamental Theories for Communications
machine learning,  mobile nodes,  network security,  wireless sensor networks,  

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Mobile wireless sensor networks (WSNs) are facing threats from malicious nodes that disturb packet transmissions, leading to poor mobile WSN performance. Existing studies have proposed a number of methods, such as decision tree-based classification methods and reputation based methods, to detect these malicious nodes. These methods assume that the malicious nodes follow only pre-defined attack models and have no learning ability. However, this underestimation of the capability of malicious node is inappropriate due to recent rapid progresses in machine learning technologies. In this study, we design reinforcement learning-based malicious nodes, and define a novel observation space and sparse reward function for the reinforcement learning. We also design an adaptive learning method to detect these smart malicious nodes. We construct a robust classifier, which is frequently updated, to detect these smart malicious nodes. Extensive experiments show that, in contrast to existing attack models, the developed malicious nodes can degrade network performance without being detected. We also investigate the performance of our detection method, and confirm that the method significantly outperforms the state-of-the-art methods in terms of detection accuracy and false detection rate.