2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 15 , Issue 2 , PP: 41-54, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Sameer Nooh 1 *
Doi: https://doi.org/10.54216/JISIoT.150204
Industrial Cyber-Physical System (CPS) signify a noteworthy development in industrial automation and control, combining physical and digital parts in order to improve the efficacy, trustworthiness, and functionality of numerous industrial procedures. Industrial CPS are helpful in a huge range of industries such as transportation, energy, manufacturing, and healthcare. Intrusion detection systems (IDs) assist as vigilant protectors, constantly observing network and physical modules for any illegal access, variances, or doubtful actions. They deliver initial threat recognition and prevent safety breaks and operating troubles. In addition, cryptographic protection guarantees the privacy, honesty and genuineness of data that spread across Industrial CPS systems. By utilizing innovative encryption and authentication devices, cryptographic solutions defense complex data from capture or damage preserving consistency and confidentiality of dangerous industrial procedures. The combination of these safety actions creates a strong defence device, boosting the flexibility of Industrial CPS besides developing cyber threats and protecting the reliability of vital industrial processes. This article presents a Deep Secure: An Integrated Approach to Intrusion Detection and Cryptographic Protection in Industrial CPS environment. The proposed model aims to integrate intrusion detection and cryptographic-based secure communication protocol for industrial CPS environments. The Deep Secure model comprises two major phases: intrusion detection and secure communication. Primarily, the intrusion detection process comprises a self-attention-based bidirectional long short-term memory (SA-BiLSTM) technique. Besides, the deer hunting optimization algorithm (DHOA) achieve hyperparameter tuning of the SA-BiLSTM technique. Moreover, a secure communication protocol is designed by the use of the ElGamal cryptosystem. The experimental result of the Deep Secure method was tested in terms of dissimilar measures. A comprehensive result analysis highlighted the advanced performance of the Deep Secure method when associated to other current approaches.
Cyber-Physical System , Intrusion Detection , ElGamal cryptosystem , Anomaly Detection , Cyber Attacks
[1] A.A. Nour, A. Mehbodniya, J.L. Webber, A. Bostani, B. Shah, B.Z. Ergashevich, and K. Sathishkumar, “Optimizing intrusion detection in industrial cyber-physical systems through transfer learning approaches,” Computers and Electrical Engineering, vol. 111, p. 108929, 2023.
[2] M. Umer, S. Sadiq, H. Karamti, R.M. Alhebshi, K. Alnowaiser, A.A. Eshmawi, H. Song, and I. Ashraf, “Deep learning-based intrusion detection methods in cyber-physical systems: Challenges and future trends,” Electronics, vol. 11, no. 20, p. 3326, 2022.
[3] V.F. Santos, C. Albuquerque, D. Passos, S.E. Quincozes, and D. Mossé, “Assessing machine learning techniques for intrusion detection in cyber-physical systems,” Energies, vol. 16, no. 16, p. 6058, 2023.
[4] W. Lu, “Detecting malicious attacks using principal component analysis in medical cyber-physical systems,” in Artificial Intelligence for Cyber-Physical Systems Hardening, Cham: Springer International Publishing, 2022, pp. 203–215.
[5] L. Almutairi, R. Daniel, S. Khasimbee, E.L. Lydia, S. Acharya, and H. Kim, “Quantum dwarf mongoose optimization with ensemble deep learning-based intrusion detection in cyber-physical systems,” IEEE Access, 2023.
[6] M.R. Aliabadi, M. Seltzer, M.V. Asl, and R. Ghavamizadeh, “Artinali#: An efficient intrusion detection technique for resource-constrained cyber-physical systems,” International Journal of Critical Infrastructure Protection, vol. 33, p. 100430, 2021.
[7] J.E. Efiong, B.O. Akinyemi, E.A. Olajubu, and G.A. Aderounmu, “GRASSMARLIN-based metadata extraction of cyber-physical systems intrusion detection in CyberSCADA networks,” in 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022, pp. 1122–1128.
[8] L. Almuqren, M.S. Maashi, M. Alamgeer, H. Mohsen, M.A. Hamza, and A.A. Abdelmageed, “Explainable artificial intelligence-enabled intrusion detection technique for secure cyber-physical systems,” Applied Sciences, vol. 13, no. 5, p. 3081, 2023.
[9] W. Li, Y. Wang, and J. Li, “A blockchain-enabled collaborative intrusion detection framework for SDN-assisted cyber-physical systems,” International Journal of Information Security, pp. 1–12, 2023.
[10] A.K. Dutta, R. Negi, and S.K. Shukla, “Robust multivariate anomaly-based intrusion detection system for cyber-physical systems,” in Cyber Security Cryptography and Machine Learning: 5th International Symposium, CSCML 2021, Be'er Sheva, Israel, July 8–9, 2021, vol. 5, pp. 86–93.
[11] S.M. Nagarajan, G.G. Deverajan, A.K. Bashir, R.P. Mahapatra, and M.S. Al-Numay, “IADF-CPS: Intelligent anomaly detection framework towards cyber-physical systems,” Computer Communications, vol. 188, pp. 81–89, 2022.
[12] S. Dalal, M. Poongodi, U.K. Lilhore, F. Dahan, T. Vaiyapuri, I. Keshta, S.M. Aldossary, A. Mahmoud, and S. Simaiya, “Optimized LightGBM model for security and privacy issues in cyber-physical systems,” Transactions on Emerging Telecommunications Technologies, p. e4771, 2023.
[13] M. Kalinin, E. Zavadskii, and A. Busygin, “A graph-based technique for securing the distributed cyber-physical system infrastructure,” Sensors, vol. 23, no. 21, p. 8724, 2023.
[14] G.N. Nguyen, N.H. Le Viet, M. Elhoseny, K. Shankar, B.B. Gupta, and A.A. Abd El-Latif, “Secure blockchain-enabled cyber-physical systems in healthcare using deep belief network with ResNet model,” Journal of Parallel and Distributed Computing, vol. 153, pp. 150–160, 2021.
[15] V. Jayagopal, M. Elangovan, S.S. Singaram, K.B. Shanmugam, B. Subramaniam, and S. Bhukya, “Intrusion detection system in industrial cyber-physical system using clustered federated learning,” SN Computer Science, vol. 4, no. 5, p. 452, 2023.
[16] M. Abdel-Basset, H. Hawash, and K. Sallam, “Federated threat-hunting approach for microservice-based industrial cyber-physical system,” IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 1905–1917, 2021.
[17] S.S. Eltanbouly, “Multimodal intrusion detection system for cyber-physical systems,” Master’s thesis, 2021.
[18] B. Li, Y. Xiao, Y. Shi, Q. Kong, Y. Wu, and H. Bao, “Anti-honeypot enabled optimal attack strategy for industrial cyber-physical systems,” IEEE Open Journal of the Computer Society, vol. 1, pp. 250–261, 2020.
[19] A. Manderna, S. Kumar, U. Dohare, M. Aljaidi, O. Kaiwartya, and J. Lloret, “Vehicular network intrusion detection using a cascaded deep learning approach with multi-variant metaheuristic,” Sensors, vol. 23, no. 21, p. 8772, 2023.
[20] R. Pandi Selvam, K. Narayanasamy, and M. Ilayaraja, “Efficient deer hunting optimization algorithm based spectrum sensing approach for 6G communication networks,” in AI-Enabled 6G Networks and Applications, 2023, pp. 111–129.
[21] F. Knirsch, A. Unterweger, M. Unterrainer, and D. Engel, “Comparison of the Paillier and ElGamal cryptosystems for smart grid aggregation protocols,” in ICISSP, 2020, pp. 232–239.
[22] [Online]. Available: https://www.kaggle.com/mrwellsdavid/unsw-nb15.
[23] A.I. Alzahrani, A. Al-Rasheed, A. Ksibi, M. Ayadi, M.M. Asiri, and M. Zakariah, “Anomaly detection in fog computing architectures using custom tab transformer for Internet of Things,” Electronics, vol. 11, no. 23, p. 4017, 2022.
