Pragmatic Study of Botnet Attack Detection In An IoT Environment

Abstract

A comprehensive search for primary research published between 2014 and 2023 was carried across several databases. Studies that describe the application of machine learning (ML) and deep learning techniques for if they was carried out across several databases. Studies that described the application of deep learning (DL) and machine learning (ML) methods for IoT botnet attack detection. Numerous facets of contemporary life have been transformed by the Internet of Things (IoT), including home automation, industrial control systems, healthcare, and transportation. On the other hand, as more devices become connected, security risks have also increased, especially from botnets. IoT Botnet attack detection techniques utilizing ML and DL have been developed in order to reduce these dangers. The best DL and ML techniques for IoT botnet attack detection are identified by a detailed examination of evaluation criteria, and performance measures in this systematic review. Performance metrics from well-known machine learning models are used to illustrate how well these machine learning techniques detect and stop Botnet attacks. When it comes to detecting Botnet assaults, deep learning (DL) and traditional machine learning (ML) methods perform similarly well. Furthermore, traditional machine learning systems still have challenges with real-time monitoring, timely detection and adaptability to novel attack approaches.