Development and deployment of advanced machine learning frameworks for the identification ,analysis,and neutrolzationof botnet activities throught extensive network traffic

Abstract

In this study, we offer an advanced machine learning (ML) framework for network traffic analysis-based botnet detection and neutralization. By allowing criminal actions like spamming, distributed denial of service assaults, and data theft, botnets pose a serious threat to network security. The ability of traditional botnet detection techniques, which rely on signature- and rule-based approaches, to recognize previously undiscovered botnet variants is constrained. In this paper, we introduce an innovative machine learning framework that makes use of deep learning methods to examine network traffic patterns and spot probable botnet activities. In order to categorize traffic as botnet- related or normal, the framework makes use of information derived from network traffic data and trains a convolutional neural network (CNN) model. The usefulness of the suggested architecture is demonstrated by experimental findings on a real-world network dataset, which achieve high accuracy in botnet identification while minimizing false positives. The created framework offers an effective way to identify and eliminate botnets, enhancing the general security of network settings.