Behavior analysis of malicious sensor nodes based on optimal response dynamics

GONG Junhui, HU Xiaohui, HONG Peng

（School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China）

Abstract： Wireless sensor networks are extremely vulnerable to various security threats. The intrusion detection method based on game theory can effectively balance the detection rate and energy consumption of the system. The accurate analysis of the attack behavior of malicious sensor nodes can help to configure intrusion detection system, reduce unnecessary system consumption and improve detection efficiency. However, the completely rational assumption of the traditional game model will cause the established model to be inconsistent with the actual attack and defense scenario. In order to formulate a reasonable and effective intrusion detection strategy, we introduce evolutionary game theory to establish an attack evolution game model based on optimal response dynamics, and then analyze the attack behavior of malicious sensor nodes. Theoretical analysis and simulation results show that the evolution trend of attacks is closely related to the number of malicious sensors in the network and the initial state of the strategy, and the attacker can set the initial strategy so that all malicious sensor nodes will eventually launch attacks. Our work is of great significance to guide the development of defense strategies for intrusion detection systems.

Key words： wireless sensor network; intrusion detection; malicious node; evolutionary game; optimal response dynamics

References

［1］BUTUN I, MORGERA S D, SANKAR R. A survey of intrusion detection systems in wireless sensor networks. IEEE communications surveys & tutorials, 2013, 16(1)： 266282.

［2］MITCHELL R, CHEN R. A survey of intrusion detection in wireless network applications. Computer Communications, 2014, 42： 123.

［3］MACHADO R, TEKINAY S. A survey of gametheoretic approaches in wireless sensor networks. Computer Networks, 2008, 52(16)： 30473061.

［4］SHEN S, YUE G, CAO Q, et al. A survey of game theory in wireless sensor networks security. Journal of Networks, 2011, 6(3)： 521.

［5］SHI H Y, WANG W L, KWOK N M, et al. Game theory for wireless sensor networks： a survey. Sensors, 2012, 12(7)： 90559097.

［6］ABDALZAHER M S, SEDDIK K, ELSABROUTY M, et al. Game theory meets wireless sensor networks security requirements and threats mitigation： a survey. Sensors, 2016, 16(7)： 1003.

［7］AGAH A, DAS S K, BASU K. A game theory based approach for security in wireless sensor networks//IEEE International Conference on Performance, Computing, and Communications, Apr. 17, 2004, Phoenix, AZ, USA. New York: IEEE, 2004： 259263.

［8］SHEN S, LI Y, XU H, et al. Signaling game based strategy of intrusion detection in wireless sensor networks. Computers & Mathematics with Applications, 2011, 62(6)： 24042416.

［9］HUANG J Y, LIAO I E, CHUNG Y F, et al. Shielding wireless sensor network using Markovian intrusion detection system with attack pattern mining. Information Sciences, 2013, 231： 3244.

［10］ZHOU W, YU Bin. Optimal defense strategy in WSNs based on the game of multistage intrusion detection. Journal of Electronics & Information Technology. 2018,40(1)： 6371.

［11］HAN L, ZHOU M, JIA W, et al. Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model. Information Sciences, 2019, 476： 491504.

［12］LOPEZ DELGADO M. On the effectiveness of intrusion detection strategies for wireless sensor networks： an evolutionary game approach. Ad hoc & Sensor Wireless Networks, 2017, 35(1/2): 2540.

［13］WANG X, LI Y. Bestresponse dynamic model for Gencos’ bidding strategies in regional electricity. Journal of North China Electric Power University, 2006(6)： 5154.

［14］SUBBA B, BISWAS S, KARMAKAR S. A game theory based multi layered intrusion detection framework for wireless sensor networks. International Journal of Wireless Information Networks, 2018, 25(4)： 399421.

［15］GHOSH D, SHARMA A, SHUKLA K K, et al. Globalized robust Markov perfect equilibrium for discounted stochastic games and its application on intrusion detection in wireless sensor networks： Part I—theory. Japan Journal of Industrial and Applied Mathematics, 2020, 37(1)： 283308.

［16］ZHANG W, HAN D, LI K C, et al. Wireless sensor network intrusion detection system based on MKELM. Soft Computing, 2020, 24(2)： 114.

［17］HEINZELMAN W B, CHANDRAKASAN A P, BALAKRISHNAN H. An applicationspecific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 2002, 1(4)： 660670.

基于最优反应动态的恶意传感器节点行为分析

巩俊辉， 胡晓辉， 洪鹏

（兰州交通大学 电子与信息工程学院， 甘肃 兰州 730070）

摘要:无线传感器网络极易遭受各种安全威胁， 基于博弈论的入侵检测方法能有效平衡系统的检测率和能耗， 对恶意传感器节点攻击行为的准确分析有助于更好地配置入侵检测系统， 减少不必要的系统消耗， 提升检测效率。 但是， 传统博弈模型的完全理性假设常常导致建立的模型与实际攻防场景不符， 为了能够制定合理且有效地入侵检测策略， 引入了演化博弈论， 建立了基于最优反应动态的攻击演化博弈模型， 对恶意传感器节点的攻击行为进行了分析。 理论分析和仿真实验结果表明， 攻击演化趋势与网络中恶意传感器数量的奇偶性以及策略的初始状态密切相关， 攻击者可以通过设定初始策略使所有恶意传感器节点最终都会发起攻击。 该研究对指导制定入侵检测系统的防御策略具有重要意义。

关键词:无线传感器网络； 入侵检测； 恶意节点； 演化博弈； 最优反应动态

引用格式：GONG Junhui, HU Xiaohui, HONG Peng. Behavior analysis of malicious sensor nodes based on optimal response dynamics. Journal of Measurement Science and Instrumentation， 2022， 13（1）： 96104. DOI： 10．3969／j．issn．16748042．2022．01．011

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