Hierarchical Classification of Botnet Using Lightweight CNN

Abstract

This paper addresses the persistent threat of botnet attacks on IoT devices, emphasizing their continued existence despite various conventional and deep learning methodologies developed for intrusion detection. Utilizing the Bot-IoT dataset, we propose a hierarchical CNN (HCNN) approach featuring three levels of classification. The HCNN approach, presented in this paper, consists of two networks: the non-hierarchical and the hierarchical network. The hierarchical network works by combining features obtained at a higher level with those of its descender. This combined information is subsequently fed into the following level to extract features for the descendant nodes. The overall network consists of 1790 parameters, with the hierarchical network introducing an additional 942 parameters to the existing backbone. The classification levels comprise a binary classification of normal vs attack in the first level, followed by 5 classes in the second level, and 11 classes in the third level. To assess the effectiveness of our proposed approach, we evaluate performance metrics such as Precision (P), Recall (R), F1 Score (F1), and Accuracy (Acc). Rigorous experiments are conducted to compare the performance of both the hierarchical and non-hierarchical models and existing state-of-the-art approaches, providing valuable insights into the efficiency of our proposed hierarchical CNN approach for addressing botnet attacks on IoT devices.