Equipment classification based differential game method for advanced persistent threats in Industrial Internet of Things

被引:2
|
作者
Gan, Chenquan [1 ,3 ]
Lin, Jiabin [1 ]
Huang, Da-Wen [2 ]
Zhu, Qingyi [3 ]
Tian, Liang [4 ]
Jain, Deepak Kumar [5 ,6 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China
[2] Sichuan Normal Univ, Coll Comp Sci, Chengdu 610101, Peoples R China
[3] Chongqing Univ Posts & Telecommun, Sch Cyber Secur & Informat Law, Chongqing 400065, Peoples R China
[4] Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China
[5] Dalian Univ Technol, Key Lab Intelligent Control & Optimizat Ind Equipm, Minist Educ, Sch Artifcial lntelligence, Dalian 116024, Peoples R China
[6] Symbiosis Int Univ, Symbiosis Inst Technol, Pune 412115, India
关键词
Industrial Internet of Things; Advanced Persistent Threat; Equipment classification; Differential game; Nash equilibrium;
D O I
10.1016/j.eswa.2023.121255
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper is dedicated to solving the problem of Advanced Persistent Threat (APT) attack and defense in the Industrial Internet of Things (IIoT). Due to the diversity of IIoT equipment and the inconsistency of protection capabilities, it is difficult for the existing uniform defense strategy and the random defense strategy to achieve ideal results. Considering that both attackers and defenders aim to achieve maximum benefits by paying the minimum cost, as well as the differences between devices, this paper proposes an equipment classification based differential game method for APT in IIoT. Firstly, all equipment is divided into two categories according to their protective capabilities. Secondly, the APT attack and defense process is mathematically described, and the corresponding differential game problem is formulated and analyzed theoretically. Finally, the theoretical results of this method are verified by various experiments, including the comparisons with the uniform defense strategy, the random defense strategy, and the latest model.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Stackelberg Game-Based Computation Offloading in Social and Cognitive Industrial Internet of Things
    Li, Feixiang
    Yao, Haipeng
    Du, Jun
    Jiang, Chunxiao
    Qian, Yi
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (08) : 5444 - 5455
  • [22] Defense Against Advanced Persistent Threats: A Colonel Blotto Game Approach
    Min, Minghui
    Xiao, Liang
    Xie, Caixia
    Hajimirsadeghi, Mohammad
    Mandayam, Narayan B.
    2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2017,
  • [23] Detecting Advanced Persistent Threats using Fractal Dimension based Machine Learning Classification
    Siddiqui, Sana
    Khan, Muhammad Salman
    Ferens, Ken
    Kinsner, Witold
    IWSPA'16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL WORKSHOP ON SECURITY AND PRIVACY ANALYTICS, 2016, : 64 - 69
  • [24] Defending Against Advanced Persistent Threats Using Game-Theory
    Rass, Stefan
    Koenig, Sandra
    Schauer, Stefan
    PLOS ONE, 2017, 12 (01):
  • [25] Transmission equipment testing based on internet of things
    Fu, Chao (fuchao_hbnu@163.com), 2018, Cefin Publishing House (01):
  • [26] Transmission equipment testing based on internet of things
    Fu, Chao (fuchao_hbnu@163.com), 2018, Cefin Publishing House (2018):
  • [27] Data fusion method of industrial internet of things based on fuzzy theory
    Chen Q.
    Lu C.
    International Journal of Internet Manufacturing and Services, 2023, 9 (04) : 487 - 501
  • [28] A Security Analysis Method for Industrial Internet of Things
    Mouratidis, Haralambos
    Diamantopoulou, Vasiliki
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (09) : 4093 - 4100
  • [29] Identification of Cyber Threats in Networks of Industrial Internet of Things Based on Neural Network Methods Using Memory
    V. M. Krundyshev
    Automatic Control and Computer Sciences, 2020, 54 : 900 - 906
  • [30] Identification of Cyber Threats in Networks of Industrial Internet of Things Based on Neural Network Methods Using Memory
    Krundyshev, V. M.
    AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2020, 54 (08) : 900 - 906