Botnet Attack Procrastination Using Deep Learning Algorithm

Abstract: Withthe increasing prevalence of botnet attacks poses a significant threat to modern network infrastructure, necessitating intelligent and proactive security solutions. This study proposes a deep learning-based approach to detect botnet activity using a one-dimensional Convolutional Neural Network (1D-CNN). The model is trained and evaluated on the CTU-13 dataset, which contains realistic botnet traffic, allowing for the extraction of valuable behavioral patterns from network flows. The proposed architecture leverages the strength of convolutional layers to automatically learn spatial and temporal features from preprocessed network data, eliminating the need for extensive manual feature engineering. Through rigorous training and validation, the model achieves high accuracy in classifying malicious and benign traffic. This approach not only enhances detection performance but also addresses the latency issues often encountered in traditional systems, effectively reducing delays in identifying botnet activity.