A Two-layer Fog-Cloud Intrusion Detection Model for IoT Networks

The Internet of Things (IoT) and its applications are becoming ubiquitous in our life. However, the open deployment environment and the limited resources of IoT devices make them vulnerable to cyber threats. In this paper, we investigate intrusion detection techniques to mitigate attacks that exploit IoT security vulnerabilities. We propose a machine learning-based two-layer hierarchical intrusion detection mechanism that can effectively detect intrusions in IoT networks while satisfying the IoT resource constraints. Specifically, the proposed model effectively utilizes the resources in the fog layer of the IoT network by efficiently deploying multi-layered feedforward neural networks in the fog-cloud infrastructure for detecting network attacks. With a fog layer into the picture, analysis is dynamically distributed across the fog and cloud layer thus enabling real-time analytics of traffic data closer to IoT devices and end-users. We have performed extensive experiments using two publicly available datasets to test the proposed approach. Test results show that the proposed approach outperforms existing approaches in multiple performance metrics such as accuracy, precision, recall, and F1-score. Moreover, experiments also justified the proposed model in terms of improved service time, lower delay, and optimal energy utilization.