RESEARCH ARTICLES
Intelligent Threat Detection in CPS-IoT Networks Using A Hybrid CNN-DBN Model with Saeho Optimization
Published 2025-04-20
Keywords
- IoT security,
- cyber-physical systems,
- anomaly detection,
- , deep learning,
- Deep Belief Network (DBN)
How to Cite
B Mamatha, T Praneeth Reddy, P Sofiya Parvez, S Satish Kumar, A Chaitanya Kumar, & S Abbas Illayas. (2025). Intelligent Threat Detection in CPS-IoT Networks Using A Hybrid CNN-DBN Model with Saeho Optimization. International Journal of Computational Learning & Intelligence, 4(4), 697–705. https://doi.org/10.5281/zenodo.15250855
Copyright (c) 2025 B Mamatha, T Praneeth Reddy, P Sofiya Parvez, S Satish Kumar, A Chaitanya Kumar, S Abbas Illayas
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abstract
The Internet of Things (IoT) is integral to smart cities and diverse societal applications, yet its large-scale implementation is hindered by significant security vulnerabilities and cyber threats. Conventional security measures frequently struggle to tackle the distinct challenges associated with IoT-driven cyber-physical systems, highlighting the need for advanced techniques like Deep Learning (DL) for robust anomaly detection. This research introduces an innovative framework that utilizes a hybrid classification strategy by combining a Deep Belief Network (DBN) with a Convolutional Neural Network (CNN). To enhance detection accuracy, the framework incorporates an innovative optimization technique called Seagull Adapted Elephant Herding Optimization (SAEHO). The "Hybrid Classifier + SAEHO" model processes extracted features from network traffic data, effectively distinguishing between malicious and benign activity. Experimental evaluations on two datasets demonstrate superior performance in terms of sensitivity, precision, accuracy, and specificity when compared to conventional methods. These results highlight the model’s potential in fortifying IoT security and offering a reliable mechanism for mitigating cyber threats in real-world applications.References
- Alguliyev, R., Imamverdiyev, Y., & Sukhostat, L. (2024). Hybrid DeepGCL model for cyber-attacks detection on cyber-physical systems. Neural Computing and Applications, 33(16), 10211–10226. https://doi.org/10.1007/s00521-021-05785-2
- Tahir, B., Jolfaei, A., & Tariq, M. (2023). Experience-driven attack design and federated-learning-based intrusion detection in Industry 4.0. IEEE Transactions on Industrial Informatics, 18(9), 6398–6405. https://doi.org/10.1109/TII.2021.3133384
- Abdalzaher, M. S., Fouda, M. M., Elsayed, H. A., & Salim, M. M. (2023). Toward secured IoT-based smart systems using machine learning. IEEE Access, 11, 20827–20841. https://doi.org/10.1109/ACCESS.2023.3250235
- Ruzafa-Alcázar, P., et al. (2023). Intrusion detection based on privacy-preserving federated learning for the industrial IoT. IEEE Transactions on Industrial Informatics, 19(2), 1145–1154. https://doi.org/10.1109/TII.2021.3126728
- Gul, O. M., Kulhandjian, M., Kantarci, B., Touazi, A., Ellement, C., & D’amours, C. (2023). Secure industrial IoT systems via RF fingerprinting under impaired channels with interference and noise. IEEE Access, 11, 26289–26307. https://doi.org/10.1109/ACCESS.2023.3257266
- Kodys, M., Lu, Z., Fok, K. W., & Thing, V. L. L. (2022, November). Intrusion detection in Internet of Things using convolutional neural networks. arXiv preprint arXiv:2211.10062. https://arxiv.org/abs/2211.10062
- Bensaoud, A., & Kalita, J. (2025, February). Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models. arXiv preprint arXiv:2502.11470. https://arxiv.org/abs/2502.11470
- Alotaibi, A., & Al-Haija, M. (2024). FEDDBN-IDS: Federated deep belief network-based wireless network intrusion detection system. EURASIP Journal on Information Security, 2024(1), Article 15. https://doi.org/10.1186/s13635-024-00156-5
- Chinnasamy, R., Subramanian, M., Easwaramoorthy, S. V., & Cho, J. (2025). Deep learning-driven methods for network-based intrusion detection systems: A systematic review. ICT Express, 2025(1), 181–215. https://doi.org/10.1016/j.icte.2025.01.005
- Abdalzaher, M. S., Fouda, M. M., Elsayed, H. A., & Salim, M. M. (2025). A high-performance hybrid LSTM-CNN secure architecture for IoT network intrusion detection. Scientific Reports, 15, Article 94500. https://doi.org/10.1038/s41598-025-94500-5
- Periasamy, K., Periasamy, S., Velayutham, S., Zhang, Z., Ahmed, S. T., & Jayapalan, A. (2022). A proactive model to predict osteoporosis: An artificial immune system approach. Expert Systems, 39(4), e12708.
- Ahmed, S. T., Basha, S. M., Ramachandran, M., Daneshmand, M., & Gandomi, A. H. (2023). An edge-AI-enabled autonomous connected ambulance-route resource recommendation protocol (ACA-R3) for eHealth in smart cities. IEEE Internet of Things Journal, 10(13), 11497-11506.
- Kumar, S. S., Ahmed, S. T., Sandeep, S., Madheswaran, M., & Basha, S. M. (2022). Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques. Computers, Materials & Continua, 72(1).
- Pasha, A., Ahmed, S. T., Painam, R. K., Mathivanan, S. K., Mallik, S., & Qin, H. (2024). Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease. Heliyon, 10(9).
- Sreedhar, K. S., Ahmed, S. T., & Sreejesh, G. (2022, June). An Improved Technique to Identify Fake News on Social Media Network using Supervised Machine Learning Concepts. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC) (pp. 652-658). IEEE.
- Ahmed, S. T., Fathima, A. S., Nishabai, M., & Sophia, S. (2024). Medical ChatBot assistance for primary clinical guidance using machine learning techniques. Procedia Computer Science, 233, 279-287.