Fortifying Network Security: Pioneering Hybrid Machine Learning for BotNet Attack Detection

Abstract

Abstract: With the rising sophistication of cyber threats, the detection of BotNet attacks on networks has become a perilous contest for cybersecurity. This paper proposes an innovative approach leveraging a hybrid machine learning (ML) framework for the detection of BotNet attacks in network atmosphere conquers a notable accuracy of 98.63%. By amalgamation of different strengths of ML algorithms such as KNN and Decision Trees, our approach aims to enhance the accuracy and efficiency of BotNet detection. The methodology comprises feature extraction from network traffic data, followed by training and testing of the hybrid model on labelled datasets to identify patterns revealing of BotNet activity. Experimental outcomes reveal the effectiveness of the proposed approach in precisely detecting BotNet attacks while diminishing false positives. The hybrid ML approach offers a forthcoming avenue for fortifying network security and mitigating the risks associated with BotNet threats.