Botnet Detection using Machine Learning Techniques- An Overview

Abstract

Many bot-based attacks have been recorded globally in recent years. To carry out their harmful actions, they mostly use infected devices and systems. Because of the frequency of these attacks, people are more aware of the need of bot detection in network security. Machine learning based botnet detection is a tool that can detect the presence of bots on a network. It does so by analyzing the data collected from a targeted machine. The data collected includes scenarios where the traffic was normal and the bots were present. Botnet detection is dangerous in a network because bots have an influence on a variety of domains, including cyber security, finance, health care, law enforcement, and more. Botnets are getting progressively composite and unsafe, and most existing rule-based and flow-based detection systems may not be capable of identifying bot activity efficiently and effectively. Botnet analysis is used to determine the type and nature of an attack. This can be done using a variety of machine learning algorithms. The system can help in educating people understand the importance of security and be used as a base for creating real time systems.