Machine Learning Approaches for Botnet Detection in Network Traffic

Botnets pose a significant challenge to network security, continually evolving and threatening the integrity of digital infrastructure. Traditional botnet detection methodologies have limitations, prompting the need for innovative approaches. In this paper, we propose a machine learning-based method to effectively detect botnets within network traffic, with a particular focus on IoT devices. Our approach leverages support vector machine (SVM) and regularized logistic regression (rLR) algorithms. Experimental results demonstrate the efficacy of our model in detecting botnet attacks. This research serves as a precursor to countering the daily onslaught of botnet attacks and emphasizes the importance of integrating machine learning techniques into network security.