Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks

Abstract

Owing to the distributed nature of denial-of-service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a game theoretic method is introduced, namely cooperative Game-based Fuzzy Q-learning (G-FQL). G-FQL adopts a combination of both the game theoretic approach and the fuzzy Q-learning algorithm in WSNs. It is a three-player strategy game consisting of sink nodes, a base station, and an attacker. The game performs at any time a victim node in the network receives a flooding packet as a DDoS attack beyond a specific alarm event threshold in WSN. The proposed model implements cooperative defense counter-attack scenarios for the sink node and the base station to operate as rational decision-maker players through a game theory strategy. In order to evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using NS-2 simulator. The model is subsequently compared against other existing soft computing methods, such as fuzzy logic controller, Q-learning, and fuzzy Q-learning, in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed model's attack detection and defense accuracy yield a greater improvement than existing abovementioned machine learning methods. In contrast to the Markovian game theoretic, the proposed model operates better in terms of successful defense rate. & 2014 Elsevier Ltd. All rights reserved.

DOI: 10.1016/j.engappai.2014.02.001

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@article{Shamshirband2014CooperativeGT, title={Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks}, author={Shahaboddin Shamshirband and Ahmed Patel and Nor Badrul Anuar and Miss Laiha Mat Kiah and Ajith Abraham}, journal={Eng. Appl. of AI}, year={2014}, volume={32}, pages={228-241} }