Authors: Dr. Vimala Roselin J, Jyothi V Kulkarni
Abstract: The explosive growth in IoT devices has created new cybersecurity challenges with an estimated 40 billion devices by 2030, with about 50% being vulnerable to cyber-attacks. The traditional centralized Intrusion Detection Systems (IDS) are faced with significant challenges such as com-munication bottlenecks, point-of-failure, and issues with privacy protection of data. In this paper, a novel cyber defense architecture is presented utilizing federated deep learning approach that incorporates hybrid deep neural networks along with privacy protections for distributed IoT networked devices. The proposed architecture makes use of a three-layer system architecture comprising IoT devices, edge gateways, and cloud cooperation using CNN, BiLSTM, and auto-encoders using a federated learning framework. Evaluation results have demonstrated the perfor-mance superiority of the proposed architecture with an accuracy of more than 99% in detecting anomalies, a 67% reduction in communication overhead, and effective resilience to adversarial attacks due to use of differential privacy.
DOI: http://doi.org/10.5281/zenodo.21338900
