A Hybrid Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks: Integrating Federated Learning and Attention Mechanisms

Dr Rania Nafea

A Hybrid Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks: Integrating Federated Learning and Attention Mechanisms

Authors: Dr Rania Nafea

Published: 2025-04-28 19:19:31.235

Abstract

The proliferation of Internet of Things (IoT) devices has created a vast and vulnerable attack surface, making robust intrusion detection systems (IDS) paramount. Traditional centralized IDS solutions face scalability and privacy challenges in the distributed IoT environment. This paper proposes a novel hybrid deep learning framework that integrates federated learning and attention mechanisms for enhanced intrusion detection in IoT networks. The framework leverages the decentralized nature of federated learning to train a global model collaboratively across IoT devices without sharing sensitive data. Attention mechanisms are incorporated within the deep learning architecture to focus on the most relevant features for accurate anomaly detection. We implement and evaluate the proposed framework using a benchmark IoT intrusion detection dataset, demonstrating significant improvements in detection accuracy, reduced communication overhead, and enhanced privacy compared to existing state-of-the-art approaches. The results showcase the potential of this hybrid approach for building resilient and privacy-preserving security solutions for the rapidly expanding IoT landscape.

Keywords

Intrusion Detection Systems, IoT Security, Federated Learning, Deep Learning, Attention Mechanisms, Anomaly Detection, Network Security, Cybersecurity, Hybrid Models

References

Butun, I., Österberg, P., & Dawes, N. W. (2014). A survey of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 16(1), 266-282.
Lazarevic, A., Ertoz, L., Ozgur, A., Srivastava, J., & Kumar, V. (2003). A comparative study of anomaly detection schemes in network intrusion detection. Proceedings of the 2003 SIAM International Conference on Data Mining, 25-36.
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & El-Latif, A. A. (2019). Deep learning approaches for intelligent intrusion detection. IEEE Access, 7, 41525-41550.
Potluri, S. R., & Diedrich, C. (2020). Intrusion detection system for internet of things using deep learning algorithms. Journal of Information Security and Applications, 55, 102597.
Hard, A., Rao, K., Ramage, D., & Beutel, A. (2019). Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604.
Ammar, M., Russello, G., & Jurdak, R. (2019). Federated learning for intrusion detection in IoT networks. 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 426-434.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Zhang, Y., Chen, X., Jiang, J., Liu, Y., & Tian, Z. (2020). An attention-based LSTM network for intrusion detection. IEEE Access, 8, 61972-61981.
Hindy, N. M., Brosset, D., Bayne, E., Seeam, A., & Tachtatzis, C. (2020). A hybrid intrusion detection system based on signature and anomaly detection for identifying IoT cyber-attacks. Sensors, 20(18), 5133.
Stiawan, Y., Malik, A. Z., Riadi, I., & Idris, M. Y. I. B. (2021). Edge computing based AI-IDS for IoT security: a review. Journal of King Saud University-Computer and Information Sciences, 33(8), 1022-1032.
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. 2010 IEEE Symposium on Security and Privacy, 305-316.
Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset: feature analysis using genetic algorithm. Information and Computer Security.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics*, 1273-1282.

Download PDF

How to Cite

Dr Rania Nafea, (2025-04-28 19:19:31.235). A Hybrid Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks: Integrating Federated Learning and Attention Mechanisms. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 2.

ISSN: 3048-6815

JANOLI International Journal of
Artificial Intelligence and its Applications ( JIJAIA )