Dimensionality Reduction for Machine Learning Based IoT Botnet Detection

The rapid development of the internet of things caused severe security problems such as the cyber attacks launched by extremely huge botnets comprised of IoT devices. The detection of these devices is essential for protecting the networks. Recently, some of the studies have demonstrated the high accuracy of machine learning methods, including deep learning, in detecting IoT botnets. However, the minimizing of the required features for classification is highly needed for overcoming scalability and computation resource problems in IoT environments. Having results which can be readily interpretable by cyber security analysts and producing signatures for the contemporary intrusion detection or network monitoring systems are other significant factors in this area in which quick and widespread security adaption is highly required. In this study, we applied feature selection to minimize the number of features in detecting the IoT bots. It is shown that fewer features can achieve very high accuracy rates and afford interpretable results with a multi-class classifier based on a shallow method, decision tree.