Botnet Attack Detection using Machine Learning
Mustafa Alshamkhany, Wisam Alshamkhany, Mohamed Kamal Mansour, Mueez Khan, Salam Dhou, Fadi Aloul · 2020
With the advancement of computers and technology, security threats are also evolving at a fast pace. Botnets are one such security threat which requires a high level of research and focus in order to be eliminated. In this paper, we use machine learning to detect Botnet attacks. Using the Bot-IoT and University of New South Wales (UNSW) datasets, four machine learning models based on four classifiers are built: Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Trees. Using 82,000 records from UNSW-NB15 dataset, the decision trees model has yielded the best overall results with 99.89% testing accuracy, 100% precision, 100% recall, and 100% F-score in detecting botnet attacks.