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

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Author(s): Prachi (The NorthCap University, India)
Copyright: 2018
Pages: 16
Source title: Handbook of Research on Network Forensics and Analysis Techniques
Source Author(s)/Editor(s): Gulshan Shrivastava (National Institute of Technology Patna, India), Prabhat Kumar (National Institute of Technology Patna, India), B. B. Gupta (National Institute of Technology Kurukshetra, India), Suman Bala (Orange Labs, France)and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)
DOI: 10.4018/978-1-5225-4100-4.ch007

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / 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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