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IoT Security Situational Awareness Based on Q-Learning and Bayesian Game

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Data Science (ICPCSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1452))

Abstract

IoT security is very crucial to IoT applications, and security situational awareness can assess the overall security status of the IoT. Traditional situational awareness methods only consider the unilateral impact of attack or defense, but lackconsideration of joint actions by both parties. Applying game theory to security situational awareness can measure the impact of the opposition and interdependence of the offensive and defensive parties. This paper proposes an IoT security situational awareness method based on Q-Learning and Bayesian game. Through Q-Learning update, the long-term benefits of action strategies in specific states were obtained, and static Bayesian game methods were used to solve the Bayesian Nash Equilibrium of participants of different types. The proposed method comprehensively considers offensive and defensive actions, obtains optimal defense decisions in multi-state and multi-type situations, and evaluates security situation. Experimental results prove the effectiveness of this method.

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Acknowledgment

This work is supported by the National Key Research and Development Program of China (No. 2017YFB1400700).

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Authors and Affiliations

  1. School of Information, Central University of Finance and Economics, Beijing, 100081, China

    Yang Li, Tianying Liu, Jianming Zhu & Xiuli Wang

  2. Engineering Research Center of State Financial Security, Ministry of Education, Central University of Finance and Economics, Beijing, 102206, China

    Yang Li, Jianming Zhu & Xiuli Wang

Authors
  1. Yang Li
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  2. Tianying Liu
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  3. Jianming Zhu
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  4. Xiuli Wang
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Corresponding author

Correspondence to Yang Li .

Editor information

Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. North University of China, Taiyuan, China

    Pinle Qin

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Li, Y., Liu, T., Zhu, J., Wang, X. (2021). IoT Security Situational Awareness Based on Q-Learning and Bayesian Game. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1452. Springer, Singapore. https://doi.org/10.1007/978-981-16-5943-0_16

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  • DOI: https://doi.org/10.1007/978-981-16-5943-0_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5942-3

  • Online ISBN: 978-981-16-5943-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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