Memory efficient federated deep learning for intrusion detection in IoT networks.

Zakariyya, Idris; Kalutarage, Harsha; Al-Kadri, M. Omar

Abstract

Deep Neural Networks (DNNs) methods are widely proposed for cyber security monitoring. However, training DNNs requires a lot of computational resources. This restricts direct deployment of DNNs to resource-constrained environments like the Internet of Things (IoT), especially in federated learning settings that train an algorithm across multiple decentralized edge devices. Therefore, this paper proposes a memory efficient method of training a Fully Connected Neural Network (FCNN) for IoT security monitoring in federated learning settings. The model‘s performance was evaluated against eleven realistic IoT benchmark datasets. Experimental results show that the proposed method can reduce memory requirement by up to 99.46 percentage points when compared to its benchmark counterpart, while maintaining the state-of-the-art accuracy and F1 score.

Citation

ZAKARIYYA, A. KALUTARAGE, H. and AL-KADRI, M.O. 2021. Memory efficient federated deep learning for intrusion detection in IoT networks. In Sani, S. and Kalutarage, H. (eds.) AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021), 14 December 2021, [virtual event]. CEUR workshop proceedings, 3125. Aachen: CEUR-WS [online], pages 85-99. Available from: http://ceur-ws.org/Vol-3125/paper7.pdf

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021)
• Start Date: Dec 14, 2021
• Acceptance Date: Nov 21, 2021
• Online Publication Date: Dec 14, 2021
• Publication Date: Apr 17, 2022
• Deposit Date: May 6, 2022
• Publicly Available Date: May 6, 2022
• Publisher: CEUR-WS
• Peer Reviewed: Peer Reviewed
• Pages: 85-99
• Series Title: CEUR workshop proceedings
• Series Number: 3125
• Series ISSN: 1613-0073
• Book Title: AI and cybersecurity 2021
• Keywords: Deep neural networks (DNNs); Internet of Things (IoT); Fully connected neural network (FCNN); Memory; Federated learning; Intrusion detection
• Public URL: https://rgu-repository.worktribe.com/output/1654366
• Publisher URL: http://ceur-ws.org/Vol-3125/