International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 18, No. 4, 8 Aug. 2026
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Federated Learning-Enabled Intrusion Detection with Bio-Inspired Feature Optimization and Hybrid Deep Neural Classifier
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Author(s)
Index Terms
IDS, Federated Learning, IoT Security, Hybrid Feature Selection, Spike Gated Linear Unit (SGLU) and Temporal Convolutional Network (TCN)
Abstract
The growth of Internet of Things (IoT) networks has drastically improved attack surface, requiring intrusion detection systems (IDS) to ensure accuracy and privacy protection. To overcome these obstacles, we introduce a federated learning (FL) based IDSW that incorporates state-of-the-art preprocessing, smart feature optimization, and a new classification paradigm. During preprocessing, raw traffic data is subject to scrubbing at a vigorous level, normalization through scaling, and label encoding to maintain consistency and reduce noise in heterogeneous local datasets. For feature selection, the Hybrid Emperor Penguin–Quokka Swarm Optimization (HEPQSO) approach is utilized which balances exploitation and exploration to find the most discriminative features while addressing the dimensionality problem. These features are then utilized by a deep hybrid classifier where the Spike Gated Linear Unit (SGLU) facilitates non-linear representation learning, and a Vision Transformer-Temporal Convolutional Network (ViT–TCN) hybrid discovers both global spatial relationships and local temporal dynamics of intrusion patterns. Experimental analyses performed using benchmark intrusion detection datasets show that the system has a high performance compared to baseline models at all times, with an accuracy of 97.88%, precision of 96.16%, recall of 97.54%, F1-score of 97.39%, specificity of 97.62%, and MCC of 97.04%, thus proving its efficiency for safe IoT settings. This combination of state-of-the-art preprocessing, hybrid feature selection, and deep federated classification forms a robust IDS that can tackle the changing landscape of cyber intrusions.
Cite This Paper
S. Shiva Prakash, M. Sunil Kumar, "Federated Learning-Enabled Intrusion Detection with Bio-Inspired Feature Optimization and Hybrid Deep Neural Classifier", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.4, pp.105-127, 2026. DOI:10.5815/ijitcs.2026.04.07
Reference
[1]J. Shen, W. Yang, Z. Chu, J. Fan, D. Niyato, and K. Y. Lam, “Effective intrusion detection in heterogeneous Internet-of-Things networks via ensemble knowledge distillation-based federated learning,” in Proc. IEEE Int. Conf. Communications (ICC), pp. 2034–2039, Jun. 2024.
[2]R. Lazzarini, H. Tianfield, and V. Charissis, “Federated learning for IoT intrusion detection,” AI, vol. 4, no. 3, pp. 509–530, 2023.
[3]I. Ioannou, P. Nagaradjane, P. Angin, P. Balasubramanian, K. J. Kavitha, P. Murugan, and V. Vassiliou, “GEMLIDS-MIoT: A green effective machine learning intrusion detection system based on federated learning for medical IoT network security hardening,” Computer Communications, vol. 218, pp. 209–239, 2024.
[4]R. Beuran, “FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection,” Computers & Security, vol. 151, p. 104337, 2025.
[5]S. Hajj, J. Azar, J. Bou Abdo, J. Demerjian, C. Guyeux, A. Makhoul, and D. Ginhac, “Cross-layer federated learning for lightweight IoT intrusion detection systems,” Sensors, vol. 23, no. 16, p. 7038, 2023.
[6]K. Begum, M. A. I. Mozumder, M. I. Joo, and H. C. Kim, “BFLIDS: Blockchain-driven federated learning for intrusion detection in IoMT networks,” Sensors, vol. 24, no. 14, p. 4591, 2024.
[7]D. Torre, A. Chennamaneni, J. Jo, G. Vyas, and B. Sabrsula, “Toward enhancing privacy preservation of a federated learning CNN intrusion detection system in IoT: Method and empirical study,” ACM Trans. Software Engineering and Methodology, vol. 34, no. 2, pp. 1–48, 2025.
[8]J. Oliveira, R. Meneguette, V. Gonçalves, D. Guidoni, and R. T. de Sousa Junior, “F-NIDS: A network intrusion detection system based on federated learning,” 2023.
[9]Q. H. Nguyen, S. Hore, A. Shah, T. Le, and N. D. Bastian, “FedNIDS: A federated learning framework for packet-based network intrusion detection system,” Digital Threats: Research and Practice, vol. 6, no. 1, pp. 1–23, 2025.
[10]T. Ohtani, R. Yamamoto, and S. Ohzahata, “IDAC: Federated learning-based intrusion detection using autonomously extracted anomalies in IoT,” Sensors, vol. 24, no. 10, p. 3218, 2024.
[11]R. R. Dos Santos, E. K. Viegas, A. O. Santin, and P. Tedeschi, “Federated learning for reliable model updates in network-based intrusion detection,” Computers & Security, vol. 133, p. 103413, 2023.
[12]G. de Carvalho Bertoli, L. A. P. Junior, O. Saotome, and A. L. Dos Santos, “Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach,” Computers & Security, vol. 127, p. 103106, 2023.
[13]T. T. Thein, Y. Shiraishi, and M. Morii, “Personalized federated learning-based intrusion detection system: Poisoning attack and defense,” Future Generation Computer Systems, vol. 153, pp. 182–192, 2024.
[14]J. L. Hernandez-Ramos, G. Karopoulos, E. Chatzoglou, V. Kouliaridis, E. Marmol, A. Gonzalez-Vidal, and G. Kambourakis, “Intrusion Detection Based on Federated Learning: A Systematic Review,” ACM Computing Surveys, vol. 57, no. 12, pp. 1–65, 2025.
[15]B. Olanrewaju-George and B. Pranggono, “Federated learning-based intrusion detection system for the Internet of Things using unsupervised and supervised deep learning models,” Cyber Security and Applications, vol. 3, p. 100068, 2025.
[16]M. Sarhan, S. Layeghy, N. Moustafa, and M. Portmann, “Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection,” Journal of Network and Systems Management, vol. 31, no. 1, p. 3, 2023.
[17]R. Dhakal, W. Raza, V. Tummala, and L. N. Kandel, “Enhancing intrusion detection in IoT networks through federated learning,” IEEE Access, 2024.
[18]M. H. Bhavsar, Y. B. Bekele, K. Roy, J. C. Kelly, and D. Limbrick, “FL-IDS: Federated learning-based intrusion detection system using edge devices for transportation IoT,” IEEE Access, vol. 12, pp. 52215–52226, 2024.
[19]Y. Alhasawi and S. Alghamdi, “Federated learning for decentralized DDoS attack detection in IoT networks,” IEEE Access, vol. 12, pp. 42357–42368, 2024.
[20]M. Umair, W. H. Tan, and Y. L. Foo, “Dynamic federated learning aggregation for enhanced intrusion detection in IoT attacks,” in Proc. Int. Conf. Artificial Intelligence in Information and Communication (ICAIIC), pp. 524–529, Feb. 2024.
[21]D. Javeed, M. S. Saeed, M. Adil, P. Kumar, and A. Jolfaei, “A federated learning-based zero trust intrusion detection system for Internet of Things,” Ad Hoc Networks, vol. 162, p. 103540, 2024.
[22]M. Althunayyan, A. Javed, and O. Rana, “A robust multi-stage intrusion detection system for in-vehicle network security using hierarchical federated learning,” Vehicular Communications, vol. 49, p. 100837, 2024.
[23]J. Chen, Y. Zhao, Q. Li, X. Feng, and K. Xu, “FedDef: Defense against gradient leakage in federated learning-based network intrusion detection systems,” IEEE Trans. Information Forensics and Security, vol. 18, pp. 4561–4576, 2023.
[24]S. Sun, P. Sharma, K. Nwodo, A. Stavrou, and H. Wang, “FedMADE: Robust federated learning for intrusion detection in IoT networks using a dynamic aggregation method,” in Proc. Int. Conf. Information Security, pp. 286–306, Oct. 2024.
[25]S. M. S. Bukhari, M. H. Zafar, M. Abou Houran, S. K. R. Moosavi, M. Mansoor, M. Muaaz, and F. Sanfilippo, “Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability,” Ad Hoc Networks, vol. 155, p. 103407, 2024.
[26]M. J. Idrissi et al., “FED-ANIDS: Federated learning for anomaly-based network intrusion detection systems,” Expert Systems with Applications, vol. 234, p. 121000, 2023.
[27]R. W. Anwar, M. Abrar, A. Salam, and F. Ullah, “Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: A multi-dataset analysis,” PeerJ Computer Science, vol. 11, p. e2751, 2025.
[28]R. Bensaid, N. Labraoui, A. A. A. Ari, H. Saidi, J. H. M. Emati, and L. Maglaras, “SA-FLIDS: Secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcare,” PeerJ Computer Science, vol. 10, p. e2414, 2024.
[29]A. Karunamurthy, K. Vijayan, P. R. Kshirsagar, and K. T. Tan, “An optimal federated learning-based intrusion detection for IoT environment,” Scientific Reports, vol. 15, no. 1, p. 8696, 2025.
[30]J. Huang, Z. Chen, S. Z. Liu, H. Zhang, and H. X. Long, “Improved intrusion detection based on hybrid deep learning models and federated learning,” Sensors, vol. 24, no. 12, p. 4002, 2024.
[31]S. Alsaleh, M. E. B. Menai, and S. Al-Ahmadi, “A heterogeneity-aware semi-decentralized model for a lightweight intrusion detection system for IoT networks based on federated learning and BiLSTM,” Sensors, vol. 25, no. 4, p. 1039, 2025.
[32]N. Chaurasia, M. Ram, P. Verma, N. Mehta, and N. Bharot, “A federated learning approach to network intrusion detection using residual networks in industrial IoT networks,” 2024.
[33]M. M. Rashid, S. U. Khan, F. Eusufzai, M. A. Redwan, S. R. Sabuj, and M. Elsharief, “A federated learning-based approach for improving intrusion detection in industrial Internet of Things networks,” Network, vol. 3, no. 1, pp. 158–179, 2023.
[34]A. Belenguer, J. A. Pascual, and J. Navaridas, “GöwFed: A novel federated network intrusion detection system,” Journal of Network and Computer Applications, vol. 217, p. 103653, 2023.
[35]O. K. ChandraUmakantham, S. Gajendran, and S. Marappan, “Enhancing intrusion detection through federated learning with enhanced Ghost_BiNet and homomorphic encryption,” IEEE Access, vol. 12, pp. 24879–24893, 2024.