JACIII Vol.28 No.1 pp. 141-149
doi: 10.20965/jaciii.2024.p0141

Research Paper:

Dynamic Real-Time Analysis of Network Attacks Based on Dynamic Risk Probability Algorithm

Chao Wang, Jiahan Dong, Guangxin Guo, Bowen Li, and Tianyu Ren

State Grid Beijing Electric Power Research Institute
No.30 South Third Ring Middle Road, Fengtai District, Beijing 100051, China

July 27, 2022
September 7, 2023
January 20, 2024
network attack, system logging, dynamic analysis, wind direction probability, threat detection

With the rapid development of Internet technology and its application, the existence of network vulnerabilities is very common. Attackers may use the defects of software, hardware, or system security policy in the network system to access or destroy the system without authorization. How to nip in the bud and carry out a safety risk assessment and early warning is an urgent problem to be solved. Based on the overall assessment of the risk factors in the whole network, the more dangerous nodes are found and priority measures are taken. The method proposed in this paper can reflect and predict the actions of attackers, repair, and adjust the previously predicted probability. It is compared with the method that evaluates the uncertainty in the network solely by calculating the static probability. The proposed new ideas and methods better reflect the real-time changes in the actual environment of the Internet, thereby better responding to the actual situation. This method can be well applied to threat detection, threat analysis, and risk assessment of monitoring system networks, enabling monitoring network managers to evaluate and protect the security of real-time power grids. It is of great significance to effectively defend against network attacks, ensure system security, and study the resistance of control systems under network attacks.

Combine forward and backward

Combine forward and backward

Cite this article as:
C. Wang, J. Dong, G. Guo, B. Li, and T. Ren, “Dynamic Real-Time Analysis of Network Attacks Based on Dynamic Risk Probability Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.1, pp. 141-149, 2024.
Data files:
  1. [1] G. Asaamoning et al., “Drone swarms as networked control systems by integration of networking and computing,” Sensors, Vol.21, No.8, Article No.2642, 2021.
  2. [2] Y. Naung et al., “Implementation of data driven control system of DC motor by using system identification process,” 2018 IEEE Conf. of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1801-1804, 2018.
  3. [3] R. Mali and S. M. Sundaram, “Design of graphical user interface (GUI) for modeling and control of interacting tank level control system,” 2017 Int. Conf. on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 1020-1023, 2017.
  4. [4] J. Li et al., “PCR instrument temperature control system based on multimodal control,” 2020 Chinese Control and Decision Conf. (CCDC), pp. 149-154, 2020.
  5. [5] Z. Chen et al., “Research on high precision control of joint position servo system for hydraulic quadruped robot,” 2019 Chinese Control Conf. (CCC), pp. 755-760, 2019.
  6. [6] H. Li et al., “Event-triggered remote dynamic control for network control systems,” 2020 16th Int. Conf. on Control, Automation, Robotics and Vision (ICARCV), pp. 483-488, 2020.
  7. [7] J. Zhao, P. Zeng, and H. Yuan, “Research on repair method in Industrial Internet of Things based on attack anomaly characteristics,” Information Technology and Network Security, Vol.41, No.4, pp. 18-24, 2022 (in Chinese).
  8. [8] A. Zimba, H. Chen, and Z. Wang, “Bayesian network based weighted APT attack paths modeling in cloud computing,” Future Generation Computer Systems, Vol.96, pp. 525-537, 2019.
  9. [9] Y. Pan and G.-H. Yang, “Event-triggered fault detection filter design for nonlinear networked systems,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol.48, No.11, pp. 1851-1862, 2018.
  10. [10] H. Li et al., “Event-triggered fault detection of nonlinear networked systems,” IEEE Trans. on Cybernetics, Vol.47, No.4, pp. 1041-1052, 2017.
  11. [11] Y. Deng, X. Yin, and S. Hu, “Event-triggered predictive control for networked control systems with DoS attacks,” Information Sciences, Vol.542, pp. 71-91, 2021.
  12. [12] C. Peng, J. Li, and M. Fei, “Resilient event-triggering H load frequency control for multi-area power systems with energy-limited DoS attacks,” IEEE Trans. on Power Systems, Vol.32, No.5, pp. 4110-4118, 2017.
  13. [13] H. Sun and C. Peng, “Relaxed event-triggered control of networked control systems under denial of service attacks,” Proc. of the 7th Int. Conf. on Artificial Intelligence and Mobile Services (AIMS 2018), pp. 141-154, 2018.
  14. [14] X. Chen and Y. Wang, “Event-triggered attack-tolerant tracking control design for networked nonlinear control systems under DoS jamming attacks,” Science China Information Sciences, Vol.63, No.5, Article No.150207, 2020.
  15. [15] D. Ding et al., “A survey on model-based distributed control and filtering for industrial cyber-physical systems,” IEEE Trans. on Industrial Informatics, Vol.15, No.5, pp. 2483-2499, 2019.
  16. [16] H. Niu et al., “An optimal hybrid learning approach for attack detection in linear networked control systems,” IEEE/CAA J. of Automatica Sinica, Vol.6, No.6, pp. 1404-1416, 2019.
  17. [17] C. Chen et al., “Adaptive synchronization of multi-agent systems with resilience to communication link faults,” Automatica, Vol.111, Article No.108636, 2020.
  18. [18] C.-H. Xie and G.-H. Yang, “Secure estimation for cyber-physical systems under adversarial actuator attacks,” IET Control Theory & Applications, Vol.11, No.17, pp. 2939-2946, 2017.
  19. [19] J. Li et al., “Passivity-based event-triggered fault tolerant control for nonlinear networked control system with actuator failures and DoS jamming attacks,” J. of the Franklin Institute, Vol.357, No.14, pp. 9288-9307, 2020.
  20. [20] L. An and G.-H. Yang, “LQ secure control for cyber-physical systems against sparse sensor and actuator attacks,” IEEE Trans. on Control of Network Systems, Vol.6, No.2, pp. 833-841, 2019.
  21. [21] X. Jin, W. M. Haddad, and T. Yucelen, “An adaptive control architecture for mitigating sensor and actuator attacks in cyber-physical systems,” IEEE Trans. on Automatic Control, Vol.62, No.11, pp. 6058-6064, 2017.
  22. [22] G. Wu, J. Sun, and J. Chen, “Optimal data injection attacks in cyber-physical systems,” IEEE Trans. on Cybernetics, Vol.48, No.12, pp. 3302-3312, 2018.
  23. [23] M. Liu et al., “Fault estimation sliding-mode observer with digital communication constraints,” IEEE Trans. on Automatic Control, Vol.63, No.10, pp. 3434-3441, 2018.
  24. [24] Y. Yu, T. Zhang, and Y. Zhao, “Optimization strategy of multiarea interconnected integrated energy system based on consistency theory,” Mobile Information Systems, Vol.2020, Article No.8884525, 2020.
  25. [25] Q. Yao et al., “Active power dispatch strategy of the wind farm based on improved multi-agent consistency algorithm,” IET Renewable Power Generation, Vol.13, No.14, pp. 2693-2704, 2019.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024