Volume 15 , Issue 2 , PP: 87-99, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Omar Ahmed Abdulkader 1 * , Muhammad Jawad Ikram 2
Doi: https://doi.org/10.54216/JCIM.150208
Cyber-physical systems (CPS) are significant to main organizations like Smart Grids and water conduct and are gradually helpless to an extensive range of developing threats. Identifying threats to CPS is of greatest significance, owing to their progressive frequent usage in numerous critical assets. Traditional safety devices like firewalls and encryption are frequently insufficient for CPS designs; the execution of Intrusion Detection Systems (IDSs) personalized for CPS is a crucial plan for safeguarding them. Artificial intelligence (AI) techniques have shown abundant probability in numerous areas of network security, mainly in network traffic observation and in the recognition of unauthorized access, misuse, or denial of network resources. IDS in CPSs and other fields namely the Internet of Things, is regularly considered through deep learning (DL) and machine learning (ML). This manuscript offers the design of an Advanced Threat Detection utilizing the Lemurs Optimization Algorithm with Deep Learning (ATD-LOADL) methodology in the CPS platform. The primary of the ATD-LOADL methodology is to focus on the recognition and classification of cyber threats in CPS. In the preliminary phase, the pre-processing of the CPS data takes place using a min-max scaler. To select an optimum set of features, the ATD-LOADL technique uses LOA as a feature selection approach. For threat detection, the ATD-LOADL algorithm uses a multi-head attention-based long short-term memory (MHA-LSTM) classifier. At last, the detection results of the MHA-LSTM method are boosted by the use of the shuffled frog leap algorithm (SFLA). The experimentation outcomes of the ATD-LOADL approach can be widely investigated on a benchmark CPS dataset. An experimentation outcome stated the enhanced threat detection results of the ATD-LOADL technique over other existing approaches
Cyber-Physical System , Threat Detection , Lemurs Optimization Algorithm , Deep Learning , Hyperparameter Tuning
[1] H. Liu, S. Wang, and Y. Li, ‘‘Event-triggered control and proactive defense for cyber–physical systems,’’ IEEE Trans. Syst., Man, Cybern. Syst., vol. 52, no. 10, pp. 6305–6313, Oct. 2022.
[2] S. Yadav and R. Kalpana, ‘‘A survey on network intrusion detection using deep generative networks for cyber-physical systems,’’ in Artificial Intelligence Paradigms for Smart Cyber-Physical Systems. Hershey, PA, USA: IGI Global, 2021, pp. 137–159.
[3] M. A. Alohali, F. N. Al-Wesabi, A. M. Hilal, S. Goel, D. Gupta, and A. Khanna, ‘‘Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment,’’ Cognit. Neurodynamics, vol. 16, pp. 1–13, Oct. 2022.
[4] T. Zoppi, M. Gharib, M. Atif, and A. Bondavalli, ‘‘Meta-learning to improve unsupervised intrusion detection in cyber-physical systems,’’ ACM Trans. Cyber-Phys. Syst., vol. 5, no. 4, pp. 1–27, 2021.
[5] D. Levonevskiy and A. Motienko, ‘‘Modeling tasks of patient assistance and emergency management in medical cyber-physical systems,’’ in Proc. Comput. Methods Syst. Softw. Cham, Switzerland: Springer, 2023, pp. 299–308.
[6] P. F. de Araujo-Filho, G. Kaddoum, D. R. Campelo, A. G. Santos, D. Macêdo, and C. Zanchettin, ‘‘Intrusion detection for cyber–physical systems using generative adversarial networks in fog environment,’’ IEEE Internet Things J., vol. 8, no. 8, pp. 6247–6256, Apr. 2021.
[7] R. F. Mansour, ‘‘Artificial intelligence based optimization with deep learning model for blockchain enabled intrusion detection in CPS environment,’’ Sci. Rep., vol. 12, no. 1, p. 12937, Jul. 2022.
[8] A. E. Ibor, O. B. Okunoye, F. A. Oladeji, and K. A. Abdulsalam, ‘‘Novel hybrid model for intrusion prediction on cyber physical systems’ communication networks based on bio-inspired deep neural network structure,’’ J. Inf. Secur. Appl., vol. 65, Mar. 2022, Art. no. 103107.
[9] M. Sharma, H. Elmiligi, and F. Gebali, ‘‘A novel intrusion detection system for RPL-based cyber–physical systems,’’ IEEE Can. J. Electr. Comput. Eng., vol. 44, no. 2, pp. 246–252, Spring 2021.
[10] J. Ali, ‘‘Intrusion detection systems trends to counteract growing cyberattacks on cyber-physical systems,’’ in Proc. 22nd Int. Arab Conf. Inf. Technol. (ACIT), Dec. 2021, pp. 1–6.
[11] Sharma, A., Rani, S., Shah, S.H., Sharma, R., Yu, F. and Hassan, M.M., 2023. An Efficient Hybrid Deep Learning Model for Denial of Service Detection in Cyber Physical Systems. IEEE Transactions on Network Science and Engineering.
[12] Althobaiti, M.M., Kumar, K.P.M., Gupta, D., Kumar, S. and Mansour, R.F., 2021. An intelligent cognitive computing based intrusion detection for industrial cyber-physical systems. Measurement, 186, p.110145.
[13] Ashraf, I., Narra, M., Umer, M., Majeed, R., Sadiq, S., Javaid, F. and Rasool, N., 2022. A deep learning-based smart framework for cyber-physical and satellite system security threats detection. Electronics, 11(4), p.667.
[14] Li, Q., 2023. Threat Detection and Diagnosis in Cyber-Physical Systems with Artificial Intelligence (Doctoral dissertation, University of Georgia).
[15] Deng, W., Yang, C. and Huang, K., 2022, July. VFD-AE: Efficient Attack Detection in Industrial Cyber-Physical Systems using Vital Feature Discovery and Deep Learning Technique. In 2022 41st Chinese Control Conference (CCC) (pp. 7479-7484). IEEE.
[16] 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.
[17] Zhou, X., Almutairi, L., Alsenani, T.R. and Ahmad, M.N., 2023. Honeypot Based Industrial Threat Detection Using Game Theory in Cyber-Physical System. Journal of Grid Computing, 21(4), p.59.
[18] Duhayyim, M.A., Alissa, K.A., Alrayes, F.S., Alotaibi, S.S., Tag El Din, E.M., Abdelmageed, A.A., Yaseen, I. and Motwakel, A., 2022. Evolutionary-based deep stacked autoencoder for intrusion detection in a cloud-based cyber-physical system. Applied Sciences, 12(14), p.6875.
[19] Ozsahin, D.U., Mustapha, M.T., Mubarak, A.S., Ameen, Z.S. and Uzun, B., 2022, August. Impact of feature scaling on machine learning models for the diagnosis of diabetes. In 2022 International Conference on Artificial Intelligence in Everything (AIE) (pp. 87-94). IEEE.
[20] Ra’ed, M., Al-qudah, N.E.A., Jawarneh, M.S. and Al-Khateeb, A., 2023. A novel improved lemurs optimization algorithm for feature selection problems. Journal of King Saud University-Computer and Information Sciences, 35(8), p.101704.
[21] Saleh, H., Amer, E., Abuhmed, T., Ali, A., Al-Fuqaha, A. and El-Sappagh, S., 2023. Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data. Scientific Reports, 13(1), p.16336.
[22] Pirgazi, J., Alimoradi, M., Esmaeili Abharian, T. and Olyaee, M.H., 2019. An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets. Scientific reports, 9(1), p.18580.
[23] Almutairi, L., Daniel, R., Khasimbee, S., Lydia, E.L., Acharya, S. and Kim, H., 2023. Quantum Dwarf Mongoose Optimization with Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems. IEEE Access.
