Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm

Peer-to-peer (P2P) botnet is one of the greatest threats to digital data. It has become a common tool for performing lot malicious activities such as DDoS attacks, phishing spreading spam, identity theft, ransomware, extortion attack, and many other fraudulent activities. P2P botnets are very resilient stealthy keep mutating evade security mechanisms. Therefore, it necessary identify detect flow from normal flow. This paper uses supervised machine learning algorithms also an ensemble technique combine performances various models make predictions. To validate results, four performance metrics have been used. These accuracy, precision, recall, F1-score. Experimental results show that proposed approach delivers 99.99% 99.81% 99.11% 99.32% F1 score, which outperform previous detection approaches.