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.

物联网 (IoT) 已成为攻击者引爆大量网络攻击的迷人攻击面。分布式拒绝服务 (DDoS) 攻击已成为物联网网络中最具威胁性的攻击。在本文中，我们提出了一种攻击检测系统来识别支持雾的物联网网络中的异常活动。最初，作者详尽地研究了基于滤波器的特征选择算法的性能，包括 ReliefF、相关特征选择 (CFS)、信息增益 (IG) 和最小冗余最大相关性 (mRMR) 以及不同类别分类算法。准备了由物联网网络特定特征组成的数据集。使用突出的评估措施评估测试分类算法的性能。而且，分类器的响应时间是针对集中式和支持雾的物联网网络基础设施计算的。实验结果表明，在准确性和延迟方面，J48 分类器优于所有其他使用 mRMR 特征选择算法测试的分类器。