Strategies of attack–defense game for wireless sensor networks considering the effect of confidence level in fuzzy environment. (June 2021)

• Record Type: Journal Article
• Authors: Wu, Yingfu; Kang, Bingyi; Wu, Hao
• Is Part Of: Engineering applications of artificial intelligence. Volume 102(2021)
• Publication Date: 2021-06
• Subjects: Wireless sensor network -- Malware -- Fuzzy set -- Reliability analysis approach; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104238
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: It is a common case that Wireless Sensor Networks are attacked by malware in the real world. According to the game theory, the action of attack–defense between Wireless Sensor Network(WSN) and malware can be regarded as a game. While substantial efforts have been made to address this issue, most of these efforts have predominantly focused on the analysis of attack–defense game in the known environment. Given that the process of gaming in real world often contains a lot of fuzzy information, we extend the focus in this line by considering the fuzzy exterior environment. Specifically, we assume the WSN attack–defense Stackelberg game is in the fuzzy environment by using fuzzy variable. Then Stackelberg game theory is utilized to calculate the equilibrium solutions of the introduced m a x i m a x chance-constrained model and m i n i m a x chance-constrained model. Based on the simulation data, this study demonstrates the confidence levels and decision perspectives affect the optimal strategy of WSN and the reliability of WSN. Finally, the novel analytical method is compared with the non-fuzzy WSN attack–defense game method. The analysis shows that the novel approach is optimal in terms of predicting the behavior of malware in resisting the attack of malware.