Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis

With rapid technological progress in the Internet of Things (IoT), it has become imperative to concentrate on its security aspect. This paper represents a model that accounts for detection botnets through use machine learning algorithms. The examined anomalies, commonly referred as botnets, cluster IoT devices attempting connect network. Essentially, this exhibited transport layer data (User Datagram Protocol - UDP) generated devices. An intelligent novel comprising Random Forest Classifier with Independent Component Analysis (ICA) was proposed botnet Various algorithms were also implemented upon processed comparative analysis. experimental results state-of-the-art three different datasets, achieving up 99.99% accuracy effectively lowest prediction time 0.12 seconds without overfitting. significance study lies detecting and efficiently under all circumstances by utilizing ICA Classifier, which is simple algorithm.