Using machine learning algorithm for detection of cyber-attacks in cyber physical systems

Rasha Almajed, Amer Ibrahim, Abedallah Zaid Abualkishik, Nahia Mourad, Faris A Almansour


Network integration is common in cyber-physical systems (CPS) to allow for remote access, surveillance, and analysis. They have been exposed to cyberattacks because of their integration with an insecure network. In the event of a violation in internet security, an attacker was able to interfere with the system's functions, which might result in catastrophic consequences. As a result, detecting breaches into mission-critical CPS is a top priority. Detecting assaults on CPSs, which are increasingly being targeted by cyber criminals and cyber threats, is becoming increasingly difficult. Machine Learning (ML) and Artificial Intelligence (AI) have the potential to make these the worst of moments, but it may also be the finest of times. There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. Hence, in this paper, we propose a novel cyberattack detection framework by integrating AI and ML (ML) methods. Here, initially we collect the dataset from the CPS database and preprocess the data using normalization for removal of errors and redundant data. The features are extracted using Linear Discriminant Analysis (LDA). We have proposed Self-tuned Fuzzy Logic-based Hidden Markov Model (SFL-HMM) with Heuristic Multi-Swarm Optimization (HMS-ACO) algorithm for detection of the cyberattacks. The proposed method is evaluated using the MATLAB simulation tool and the metrics are compared with existing approaches. The results of the experiments reveal that the framework is more successful than traditional strategies in achieving high degrees of privacy. Furthermore, in terms of detection rate, false positive rate, and computing time, the framework beats traditional detection algorithms.

Full Text:



Duo, W., Zhou, M. and Abusorrah, A., 2022. A Survey of Cyberattacks on Cyber Physical Systems: Recent Advances and Challenges. IEEE/CAA Journal of Automatica Sinica, 9(5), pp.784-800.

Kwon, C. and Hwang, I., 2017. Reachability analysis for safety assurance of cyber-physical systems against cyber attacks. IEEE Transactions on Automatic Control, 63(7), pp.2272-2279.

Yang, H., Zhan, K., Kadoch, M., Liang, Y. and Cheriet, M., 2020. BLCS: Brain-like distributed control security in cyber physical systems. IEEE Network, 34(3), pp.8-15.

Prasad, R. and Rohokale, V., 2020. Artificial intelligence and machine learning in cyber security. In Cyber Security: The Lifeline of Information and Communication Technology (pp. 231-247). Springer, Cham.

Sedjelmaci, H., Guenab, F., Senouci, S.M., Moustafa, H., Liu, J. and Han, S., 2020. Cyber security based on artificial intelligence for cyber-physical systems. IEEE Network, 34(3), pp.6-7.

Tepjit, S., Horváth, I. and Rusák, Z., 2019. The state of framework development for implementing reasoning mechanisms in smart cyber-physical systems: A literature review. Journal of computational design and engineering, 6(4), pp.527-541.

Merino, T., Stillwell, M., Steele, M., Coplan, M., Patton, J., Stoyanov, A. and Deng, L., 2019, May. Expansion of cyber attack data from unbalanced datasets using generative adversarial networks. In International Conference on Software Engineering Research, Management and Applications (pp. 131-145). Springer, Cham.

Li, B., Wu, Y., Song, J., Lu, R., Li, T. and Zhao, L., 2020. DeepFed: Federated deep learning for intrusion detection in industrial cyber–physical systems. IEEE Transactions on Industrial Informatics, 17(8), pp.5615-5624.

Hussain, B., Du, Q., Sun, B. and Han, Z., 2020. Deep learning-based DDoS-attack detection for cyber–physical system over 5G network. IEEE Transactions on Industrial Informatics, 17(2), pp.860-870.

Zhang, J., Pan, L., Han, Q.L., Chen, C., Wen, S. and Xiang, Y., 2021. Deep learning based attack detection for cyber-physical system cybersecurity: A survey. IEEE/CAA Journal of AutomaticaSinica, 9(3), pp.377-391.

Skopik, F., Landauer, M., Wurzenberger, M., Vormayr, G., Milosevic, J., Fabini, J., Prüggler, W., Kruschitz, O., Widmann, B., Truckenthanner, K. and Rass, S., 2020. synERGY: Cross-correlation of operational and contextual data to timely detect and mitigate attacks to cyber-physical systems. Journal of Information Security and Applications, 54, p.102544.

Meleshko, A. V., V. A. Desnitsky, and I. V. Kotenko. "ML based approach to detection of anomalous data from sensors in cyber-physical water supply systems." In IOP conference series: materials science and engineering, vol. 709, no. 3, p. 033034. IOP Publishing, 2020.

Rajawat, A.S., Rawat, R., Shaw, R.N. and Ghosh, A., 2021. Cyber physical system fraud analysis by mobile robot. In ML for Robotics Applications (pp. 47-61). Springer, Singapore.

Wang, T., Liang, Y., Yang, Y., Xu, G., Peng, H., Liu, A. and Jia, W., 2020. An intelligent edge-computing-based method to counter coupling problems in cyber-physical systems. IEEE Network, 34(3), pp.16-22.

Maleh, Y., 2020. ML techniques for IoT intrusions detection in aerospace cyber-physical systems. In ML and Data Mining in Aerospace Technology (pp. 205-232). Springer, Cham.

Luo, Y., Xiao, Y., Cheng, L., Peng, G. and Yao, D., 2021. Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities. ACM Computing Surveys (CSUR), 54(5), pp.1-36.

Meira, J., Andrade, R., Praça, I., Carneiro, J. and Marreiros, G., 2018, June. Comparative results with unsupervised techniques in cyber attack novelty detection. In International Symposium on Ambient Intelligence (pp. 103-112). Springer, Cham.

Bouyeddou, B., Harrou, F., Kadri, B. and Sun, Y., 2021. Detecting network cyber-attacks using an integrated statistical approach. Cluster Computing, 24(2), pp.1435-1453.

Ajayi, O., Cherian, M. and Saadawi, T., 2019, August. Secured cyber-attack signatures distribution using blockchain technology. In 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) (pp. 482-488). IEEE.

de Araujo-Filho, P.F., Kaddoum, G., Campelo, D.R., Santos, A.G., Macêdo, D. and Zanchettin, C., 2020. Intrusion detection for cyber–physical systems using generative adversarial networks in fog environment. IEEE Internet of Things Journal, 8(8), pp.6247-6256.

Jamal, A.A., Majid, A.A.M., Konev, A., Kosachenko, T. and Shelupanov, A., 2021. A review on security analysis of cyber physical systems using ML. Materials Today: Proceedings.

Olowononi, F.O., Rawat, D.B. and Liu, C., 2020. Resilient ML for networked cyber physical systems: A survey for ML security to securing ML for cps. IEEE Communications Surveys & Tutorials, 23(1), pp.524-552.

Zhang, J., Pan, L., Han, Q.L., Chen, C., Wen, S. and Xiang, Y., 2021. Deep learning based attack detection for cyber-physical system cybersecurity: A survey. IEEE/CAA Journal of Automatica Sinica, 9(3), pp.377-391.

AlZubi, A.A., Al-Maitah, M. and Alarifi, A., 2021. Cyber-attack detection in healthcare using cyber-physical system and ML techniques. Soft Computing, 25(18), pp.12319-12332.

Paredes, C.M., Martínez-Castro, D., Ibarra-Junquera, V. and González-Potes, A., 2021. Detection and Isolation of DoS and Integrity Cyberattacks in Cyber-Physical Systems with a Neural Network-Based Architecture. Electronics, 10(18), p.2238.

Lu, Y., Huang, X., Dai, Y., Maharjan, S. and Zhang, Y., 2020. Federated learning for data privacy preservation in vehicular cyber-physical systems. IEEE Network, 34(3), pp.50-56.

Wan, B., Xu, C., Mahapatra, R.P. and Selvaraj, P., 2021. Understanding the Cyber-Physical System in International Stadiums for Security in the Network from Cyber-Attacks and Adversaries using AI. Wireless Personal Communications, pp.1-18.

Karimipour, H. and Leung, H., 2020. Relaxation-based anomaly detection in cyber-physical systems using ensemble kalman filter. IET Cyper-Phys. Syst.: Theory & Appl., 5(1), pp.49-58.

Thakur, S., Chakraborty, A., De, R., Kumar, N. and Sarkar, R., 2021. Intrusion detection in cyber-physical systems using a generic and domain specific deep autoencoder model. Computers & Electrical Engineering, 91, p.107044.

Tan, S., Guerrero, J.M., Xie, P., Han, R. and Vasquez, J.C., 2020. Brief survey on attack detection methods for cyber-physical systems. IEEE Systems Journal, 14(4), pp.5329-5339.

Jahromi, A.N., Karimipour, H., Dehghantanha, A. and Choo, K.K.R., 2021. Toward Detection and Attribution of Cyber-Attacks in IoT-Enabled Cyber–Physical Systems. IEEE Internet of Things Journal, 8(17), pp.13712-13722.

Farivar, F., Haghighi, M.S., Jolfaei, A. and Alazab, M., 2019. AI for detection, estimation, and compensation of malicious attacks in nonlinear cyber-physical systems and industrial IoT. IEEE transactions on industrial informatics, 16(4), pp.2716-2725.

Wu, M., Song, Z. and Moon, Y.B., 2019. Detecting cyber-physical attacks in CyberManufacturing systems with ML methods. Journal of intelligent manufacturing, 30(3), pp.1111-1123.

Abokifa, A.A., Haddad, K., Lo, C. and Biswas, P., 2019. Real-time identification of cyber-physical attacks on water distribution systems via ML–based anomaly detection techniques. Journal of Water Resources Planning and Management, 145(1), p.04018089.

Sharmeen, S., Huda, S. and Abawajy, J., 2019, August. Identifying malware on cyber physical systems by incorporating semi-supervised approach and deep learning. In IOP Conference Series: Earth and Environmental Science (Vol. 322, No. 1, p. 012012). IOP Publishing.

Yeboah-Ofori, A., Islam, S. and Brimicombe, A., 2019, May. Detecting cyber supply chain attacks on cyber physical systems using Bayesian belief network. In 2019 International Conference on Cyber Security and Internet of Things (ICSIoT) (pp. 37-42). IEEE.

Teyou, D., Kamdem, G. and Ziazet, J., 2019. Convolutional neural network for intrusion detection system in cyber physical systems. arXiv preprint arXiv:1905.03168.

Li, F., Shi, Y., Shinde, A., Ye, J. and Song, W., 2019. Enhanced cyber-physical security in internet of things through energy auditing. IEEE Internet of Things Journal, 6(3), pp.5224-5231.

Li, D., Chen, D., Jin, B., Shi, L., Goh, J. and Ng, S.K., 2019, September. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In International Conference on Artificial Neural Networks (pp. 703-716). Springer, Cham.

Ramasamy, L.K., Khan, F., Shah, M., Prasad, B.V.V.S., Iwendi, C. and Biamba, C., 2022. Secure Smart Wearable Computing through AI-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring. Sensors, 22(3), p.1076.



  • There are currently no refbacks.

Copyright (c) 2022 Rasha Almajed, Amer Ibrahim, Abedallah Zaid Abualkishik, Nahia Mourad, Faris A Almansour

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License