International Journal of Nonlinear Analysis and Applications

Review of machine learning and deep learning mechanism in cyber-physical system

Document Type : Review articles

Authors

1 ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India

2 ECE, SRM Institute of science and technology, Kattankulathur, Chennai, India

3 ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India

Abstract

Cyber-Physical Systems are one of the emerging technologies which involve the integration of cyber system physical and control systems. This Cyber-physical System automates the industrial process like manufacturing, monitoring and control. Since the system involves three different cyber, physical and control optimization domains, such systems are complex in nature and cannot be done with a traditional optimization mechanism. Machine learning and deep learning are efficient mechanisms to model the behavior of such complex systems for design and optimization. In this work, the application of machine learning mechanisms in the cyber-physical system for various purposes like security, re-organization, and scheduling. This systematic review will give more insight into the latest application and mechanism of machine learning and deep learning for the cyber-physical system.

Keywords

[1] Y. Bai, Y. Huang, G. Xie, R. Li and W. Chang, ASDYS: Dynamic scheduling using active strategies for multifunctional mixed-criticality cyber–physical systems, IEEE Trans. Indust. Inf. 17(8) (2020) 5175–5184.
[2] L. Bu, Q. Wang, X. Ren, S. Xing and X. Li, Scenario-based online reachability validation for CPS fault prediction,IEEE Trans. Computer-Aided Design Integ. Circuits Syst. 39(10) (2019) 2081–2094.
[3] N. Gupta, A. Tiwari, S.T. Bukkapatnam and R. Karri, Additive manufacturing cyber-physical system: Supply chain cybersecurity and risks, IEEE Access 8 (2020) 47322–47333.
[4] B. Hussain, Q. Du, B. Sun and Z. Han, Deep learning-based DDoS-attack detection for cyber–physical system over 5G network, IEEE Trans. Indust.Inf. 17(2) (2020) 860–870.
[5] A.A. Jamal, A.A.M. Majid, A. Konev, T. Kosachenko and A. Shelupanov, A review on security analysis of cyber physical systems using Machine learning, Materials Today: Proc. (2021).
[6] M. Keshk, E. Sitnikova, N. Moustafa, J. Hu and I. Khalil, An integrated framework for privacy-preserving based anomaly detection for cyber-physical systems, IEEE Trans. Sustainable Comput. 6(1) (2019) 66–79.
[7] S. Kwon, H. Yoo and T. Shon, IEEE 1815.1-based power system security with bidirectional RNN-based network anomalous attack detection for the cyber-physical system, IEEE Access 8 (2020) 77572–77586.
[8] A.S. Leong, A. Ramaswamy, D.E. Quevedo, H. Karl and L. Shi, Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems, Automatica 113 (2020) 108759.
[9] H. Ma, J. Tian, K. Qiu, D. Lo, D. Gao, D. Wu, C. Jia and T. Baker, Deep-learning–based app sensitive behavior surveillance for Android powered cyber–physical systems, IEEE Trans. Indust. Inf. 17(8) (2020) 5840–5850.
[10] L. Mo, P. You, X. Cao, Y. Song and A. Kritikakou, Event-driven joint mobile actuators scheduling and control in cyber-physical systems, IEEE Trans. Indust. Inf. 15(11) (2019) 5877–5891.
[11] F.O. Olowononi, D.B. Rawat and C. Liu, Resilient machine learning for networked cyber physical systems: A survey for machine learning security to securing machine learning for cps, IEEE Communic. Surv. Tutor. 23(1) (2020) 524–552.
[12] H. Peng, C. Liu, D. Zhao, H. Ye, Z. Fang and W. Wang, Security analysis of CPS systems under different swapping strategies in IoT environments, IEEE Access 8 (2020) 63567–63576.
[13] G.D. Putnik, V.K. Manupati, S.K. Pabba, L. Varela and F. Ferreira, Semi-Double-loop machine learning-based CPS approach for predictive maintenance in manufacturing system based on machine status indications, CIRP Ann. 70(1 (2021) 365–368.
[14] D. Sinha and R. Roy, Deadline-aware scheduling for maximizing information freshness in industrial cyber-physical system, IEEE Sensors J. 21(1) (2020) 381–393.
[15] G. Tertytchny, N. Nicolaou and M.K. Michael, Classifying network abnormalities into faults and attacks in IoTbased cyber physical systems using machine learning, Microproc. Microsyst. 77 (2020) 103121.
[16] A. Villalonga, E. Negri, G. Biscardo, F. Castano, R.E. Haber, L. Fumagalli and M. Macchi, A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins, Annual Rev. Cont.51 (2021) 357–373.
[17] J. Zhang and J. Sun, Optimal cooperative multiple-attackers scheduling against remote state estimation of cyberphysical systems, Syst. Cont. Lett. 144 (2020) 104771.
International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 1
March 2022
Pages 583-590
  • Receive Date: 09 June 2021
  • Revise Date: 15 August 2021
  • Accept Date: 23 September 2021