Design of Anomaly-Based Intrusion Detection System Using Fog Computing for IoT Network

With increase in the demand for Internet of Things (IoT)-based services, the capability to detect anomalies such as malicious control, spying and other threats within IoT-based network has become a major issue. Traditional Intrusion Detection Systems (IDSs) cannot be used in typical IoT-based network due to various constraints in terms of battery life, memory capacity and computational capability. In order to address these issues, various IDSs have been proposed in literature. However, most of the IDSs face problem of high false alarm rate and low accuracy in anomaly detection process. In this paper, we have proposed a anomaly-based intrusion detection system by decentralizing the existing cloud based security architecture to local fog nodes. In order to evaluate the effectiveness of the proposed model various machine learning algorithms such as Random Forest, K-Nearest Neighbor and Decision Tree are used. Performance of our proposed model is tested using actual IoT-based dataset. The evaluation of the underlying approach outperforms in high detection accuracy and low false alarm rate using Random Forest algorithm.

Automated summary

This paper proposes an anomaly-based intrusion detection system by decentralizing the existing cloud based security architecture to local fog nodes, addressing the limitations of traditional IDS in IoT networks. Various machine learning algorithms are used for evaluation, with the Random Forest algorithm outperforming in terms of detection accuracy and false alarm rate.