BotDet: A System for Real Time Botnet Command and Control Traffic Detection

Ghafir, Ibrahim; Prenosil, Vaclav; Hammoudeh, Mohammad; Baker, Thar; Jabbar, Sohail; Khalid, Shehzad; Jaf, Sardar

Abstract

Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of hackers. Sophisticated advanced persistent threats and malware have significantly contributed to increasing risks to the health sector. Many recent attacks are attributed to the spread of malicious software, e.g., ransomware or bot malware. Machines infected with bot malware can be used as tools for remote attack or even cryptomining. This paper presents a novel approach, called BotDet, for botnet Command and Control (C&C) traffic detection to defend against malware attacks in critical ultrastructure systems. There are two stages in the development of the proposed sytsem: (i) we have developed four detection modules to detect different possible techniques used in botnet C&C communications; (ii) we have designed a correlation framework to reduce the rate of false alarms raised by individual detection modules. Evaluation results show that BotDet balances the true positive rate and the false positive rate with 82.3% and 13.6% respectively. Furthermore, it proves BotDet capability of real time detection.

Citation

Ghafir, I., Prenosil, V., Hammoudeh, M., Baker, T., Jabbar, S., Khalid, S., & Jaf, S. (2018). BotDet: A System for Real Time Botnet Command and Control Traffic Detection. IEEE Access, 6, 38947-38958. https://doi.org/10.1109/access.2018.2846740

• Journal Article Type: Article
• Acceptance Date: May 26, 2018
• Online Publication Date: Jun 13, 2018
• Publication Date: Jul 30, 2018
• Deposit Date: Jun 12, 2018
• Publicly Available Date: Jul 26, 2018
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Pages: 38947-38958
• DOI: https://doi.org/10.1109/access.2018.2846740
• Public URL: https://durham-repository.worktribe.com/output/1357310