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Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01042
Number of page(s) 8
DOI https://doi.org/10.1051/itmconf/20257901042
Published online 08 October 2025
  1. A. Awajan, A novel deep learning-based intrusion detection system for IoT networks. Computers 12, 34 (2023). https://doi.org/10.3390/computers12020034 [Google Scholar]
  2. S.C. Phillips, S. Taylor, M. Boniface, S. Modafferi, M. Surridge, Automated knowledge-based cybersecurity risk assessment of cyber-physical systems. IEEE Access 12, 82482–82505 (2024). https://doi.org/10.1109/ACCESS.2024.3404264 [Google Scholar]
  3. H. Yi, S. Zhang, D. An, Z. Liu, PatchesNet: PatchTST-based multi-scale network security situation prediction. Knowl.-Based Syst. 299, 112037 (2024). https://doi.org/10.1016/j.knosys.2024.112037 [Google Scholar]
  4. J.B. Awotunde, F.E. Ayo, R. Panigrahi, A. Garg, A.K. Bhoi, P. Barsocchi, A multi-level random forest model-based intrusion detection using fuzzy inference system for Internet of Things networks. Int. J. Comput. Intell. Syst. 16, 31 (2023). https://doi.org/10.1007/s44196-023-00205-w [Google Scholar]
  5. A. Henry, S. Gautam, S. Khanna, K. Rabie, T. Shongwe, P. Bhattacharya, B. Sharma, S. Chowdhury, Composition of hybrid deep learning model and feature optimization for intrusion detection system. Sensors 23, 890 (2023). https://doi.org/10.3390/s23020890 [Google Scholar]
  6. T. Yang, J. Chen, H. Deng, B. He, A lightweight intrusion detection algorithm for IoT based on data purification and a separable convolution improved CNN. Knowl.-Based Syst. 304, 112473 (2024). https://doi.org/10.1016/j.knosys.2024.112473 [Google Scholar]
  7. S.E. Maoudj, A. Belghiat, A deep learning-based approach with two-step minority classes prediction for intrusion detection in Internet of Things networks. Knowl.-Based Syst. 312, 113143 (2025). https://doi.org/10.1016/j.knosys.2025.113143 [Google Scholar]
  8. Q. Liu, D. Wang, Y. Jia, S. Luo, C. Wang, A multitask based deep learning approach for intrusion detection. Knowl.-Based Syst. 238, 107852 (2022). https://doi.org/10.1016/j.knosys.2021.107852 [Google Scholar]
  9. T. Gaber, J.B. Awotunde, M. Torky, S.A. Ajagbe, M. Hammoudeh, W. Li, Metaverse-IDS: Deep learning-based intrusion detection system for Metaverse-IoT networks. Internet Things 24, 100977 (2023). https://doi.org/10.1016/j.iot.2023.100977 [Google Scholar]
  10. J. Zhang, R. Chen, Y. Zhang, W. Han, Z. Gu, S. Yang, Y. Fu, MF2POSE: Multi-task feature fusion pseudo-siamese network for intrusion detection using category-distance promotion loss. Knowl.- Based Syst. 283, 111110 (2024). https://doi.org/10.1016/j.knosys.2023.111110 [Google Scholar]
  11. A. Singh, P.K. Chouhan, G.S. Aujla, SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment. Ad Hoc Netw. 156, 103404 (2024). https://doi.org/10.1016/j.adhoc.2024.103404 [Google Scholar]
  12. R. Lazzarini, H. Tianfield, V. Charissis, A stacking ensemble of deep learning models for IoT intrusion detection. Knowl.-Based Syst. 279, 110941 (2023). https://doi.org/10.1016/j.knosys.2023.110941 [Google Scholar]
  13. A. Zohourian, S. Dadkhah, H. Molyneaux, E.C.P. Neto, A.A. Ghorbani, IoT-PRIDS: Leveraging packet representations for intrusion detection in IoT networks. Comput. Secur. 146, 104034 (2024). https://doi.org/10.1016/j.cose.2024.104034 [Google Scholar]
  14. A. Gueriani, H. Kheddar, A.C. Mazari, Enhancing IoT security with CNN- and LSTM-based intrusion detection systems. In 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS), IEEE, EL OUED, Algeria, April 24–25 (2024), 1–7 [Google Scholar]
  15. M. Bhavsar, K. Roy, J. Kelly, O. Olusola, Anomaly-based intrusion detection system for IoT application. Discover Internet Things 3, 5 (2023). https://doi.org/10.1007/s43926-023-00034-5 [Google Scholar]

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