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@article{1420834,
author = {Ghafir, Ibrahim and Přenosil, Václav and Hammoudeh, Mohammad and Baker, Thar and Jabbar, Sohail and Khalid, Shehzad and Jaf, Sardar},
article_number = {June},
doi = {http://dx.doi.org/10.1109/ACCESS.2018.2846740},
keywords = {critical infrastructure security; healthcare cyber attacks; malware; botnet; command and control server; intrusion detection system; alert correlation},
language = {eng},
issn = {2169-3536},
journal = {IEEE Access},
title = {BotDet: A System for Real Time Botnet Command and Control Traffic Detection},
url = {https://ieeexplore.ieee.org/document/8384239/},
volume = {6},
year = {2018}
}
TY - JOUR
ID - 1420834
AU - Ghafir, Ibrahim - Přenosil, Václav - Hammoudeh, Mohammad - Baker, Thar - Jabbar, Sohail - Khalid, Shehzad - Jaf, Sardar
PY - 2018
TI - BotDet: A System for Real Time Botnet Command and Control Traffic Detection
JF - IEEE Access
VL - 6
IS - June
SP - 38947-38958
EP - 38947-38958
PB - IEEE Xplore Digital Library
SN - 21693536
KW - critical infrastructure security
KW - healthcare cyber attacks
KW - malware
KW - botnet
KW - command and control server
KW - intrusion detection system
KW - alert correlation
UR - https://ieeexplore.ieee.org/document/8384239/
L2 - https://ieeexplore.ieee.org/document/8384239/
N2 - 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.
ER -
GHAFIR, Ibrahim, Václav PŘENOSIL, Mohammad HAMMOUDEH, Thar BAKER, Sohail JABBAR, Shehzad KHALID a Sardar JAF. BotDet: A System for Real Time Botnet Command and Control Traffic Detection. \textit{IEEE Access}. IEEE Xplore Digital Library, 2018, roč.~6, June, s.~38947-38958. ISSN~2169-3536. Dostupné z: https://dx.doi.org/10.1109/ACCESS.2018.2846740.
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