Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks

Internet-of-Things (IoT) has become an enthralling attacking surface for attackers to explode multitude of cyber-attacks. Distributed Denial of Service (DDoS) attack has transpired as the most menacing attack in the IoT networks. In this article, we propose an attack detection system to identify anomalous activities in the fog-enabled IoT network. Initially, authors have investigated exhaustively on the performance of filter-based feature selection algorithms comprising ReliefF, Correlation Feature Selection (CFS), Information Gain (IG), and Minimum-Redundancy-Maximum-Relevancy (mRMR) and distinct categories classification algorithms upon the prepared dataset consisting of IoT network specific features. Performance of the tested classification algorithm is assessed using prominent evaluation measures. Moreover, response time of classifiers is calculated for centralized and fog-enabled IoT network infrastructure. The experimental outcomes unveil that, in terms of both accuracy and latency, J48 classifier outperforms all other tested classifier with mRMR feature selection algorithm.