Performance comparison of feature selection algorithms in context of P2P botnet detection

Over the years, the use of internet has grown exponentially. As a result, crime on the internet has also grown. Botnets serve as the main technological backbone for a wide array of cyberattacks. As evident from various literatures, machine learning algorithms has a lot of potential in the detection of botnets. However, dimensionality of real-world datasets creates bottleneck in analysis. In this context, feature selection techniques have come up as a great tool in reducing the dimensionality without losing the physical interpretation of the original data. In this paper, we compare three different approaches of feature selection. We explore and compare three feature selection techniques categorised under filter, wrapper, and embedded methods. After conducting feature selection, we have employed six supervised machine learning classifiers for classification and detection of P2P botnet flows. Additionally, we have employed majority voting ensemble learning algorithm to improve the classification results.