Description

Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cyber security risks due to the lack of security for IoT devices. IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analysing the importance of botnet detection methods are limited in the IoT environment. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML) based botnet detection system. Specifically, two methods are proposed: The first unsupervised clustering method to label the data features automatically and The second feature selection method uses selecting best features. The evaluation results show that the proposed methods have improved the performance of the botnet detection. To Evaluate the performance using machine learning and Proposed deep learning algorithm to find high accuracy status.