Federated Learning-Based Intrusion Detection and Energy Optimization for IoT Networks Using Whale Optimization Algorithm

Shahzad Latif; Hamza Javed; Saira Shaheen

Authors

Shahzad Latif Department of Department of Computer Science (SZABIST University Islamabad).
Hamza Javed Department of Department of Computer Science (SZABIST University Islamabad).
Saira Shaheen Department of Department of Computer Science (SZABIST University Islamabad).

Keywords:

Internet of Things (IoT), Intrusion Detection Systems (IDS), Federated Learning (FL). Whale Optimization Algorithm (WOA).

Abstract

The IoT devices plays an important role in today’s technology. However, with rapid growth of IoT based technology, security of these devices and high power consumption are significant challenges. To mitigate these challenges, this research work proposed novel federated learning-based intrusion detection system to mitigate security issues. Moreover, to minimize energy consumption, a whale colony-based algorithm is proposed. The results are simulated and compared with other studied algorithms. The proposed algorithms outperformed the other understudied algorithms. Experimental results on the CIC-IoT-2023 dataset demonstrate that the proposed FL-WOA framework achieved 99.17% intrusion detection accuracy, outperforming existing federated learning approaches by up to 5.56%, while significantly reducing network energy consumption through optimized resource allocation.

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