Abstract
During the last decade significant scientific efforts have been invested in the development of methods that could provide efficient and effective botnet detection. As a result, an array of detection methods based on diverse technical principles and targeting various aspects of botnet phenomena have been defined. As botnets rely on the Internet for both communicating with the attacker as well as for implementing different attack campaigns, network traffic analysis is one of the main means of identifying their existence. In addition to relying on traffic analysis for botnet detection, many contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine
learning-based botnet detection systems.
learning-based botnet detection systems.
Original language | English |
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Journal | Journal of Cyber Security and Mobility |
Volume | 4 |
Issue number | 2 & 3 |
Number of pages | 32 |
ISSN | 2245-1439 |
DOIs | |
Publication status | Published - 22 Jan 2016 |
Event | CMI International Conference on Cyber Crime, Cyber Security, Privacy and Trust - AAU CPH, København, Denmark Duration: 26 Nov 2015 → 27 Nov 2015 |
Conference
Conference | CMI International Conference on Cyber Crime, Cyber Security, Privacy and Trust |
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Location | AAU CPH |
Country/Territory | Denmark |
City | København |
Period | 26/11/2015 → 27/11/2015 |
Keywords
- Botnet detection
- State of the art
- Comparative analysis
- Traffic analysis
- Machine learning