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Attackers compromise insecure IoT devices to expand their botnets in order to launch more influential attacks against their victims. In various studies, machine learning has been used to detect IoT botnet attacks. In this paper, we focus on the minimization of feature sets for machine learning tasks that are formulated as six different binary and multiclass classification problems based on the stages of the botnet life cycle. More specifically, we applied filter and wrapper methods with selected machine learning methods and derived optimal feature sets for each classification problem. The experimental results show that it is possible to achieve very high detection rates with a very limited number of features. Some wrapper methods guarantee an optimal feature set regardless of the problem formulation, but filter methods do not achieve that in all cases. The feature selection methods prefer channel-based features for detection at post-attack, communication, and control stages, while host-based features are more influential in identifying attacks originating from bots.

In-Depth Feature Selection for the Statistical Machine Learning-Based Botnet Detection in IoT Networks