Detection of Botnet Based Attacks on Network: Using Machine Learning Techniques

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Author(s): Prachi (The NorthCap University, India)
Copyright: 2021
Pages: 15
Source title: Research Anthology on Combating Denial-of-Service Attacks
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-5348-0.ch004

Keywords: Cyber and Network Security / Cyber Warfare & Terrorism / Information Science Reference / Security & Forensics

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Abstract

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.

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Detection of Botnet Based Attacks on Network: Using Machine Learning Techniques

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