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BotDet: A system for real time botnet command and control traffic detection

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journal contribution
posted on 20.08.2018, 16:13 authored by Ibrahim Ghafir, Vaclav Prenosil, Mohammad Hammoudeh, Thar Baker, Sohail Jabbar, Shehzad Khalid, Sardar Jaf
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 system: 1) we have developed four detection modules to detect different possible techniques used in botnet C&C communications and 2) 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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Access

Volume

6

Citation

GHAFIR, I. ... et al, 2018. BotDet: A system for real time botnet command and control traffic detection. IEEE Access, 6, pp.38947-38958.

Publisher

IEEE

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

Acceptance date

26/05/2018

Publication date

2018

Notes

This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/

eISSN

2169-3536

Language

en