GHAFIR, Ibrahim, Václav PŘENOSIL, Mohammad HAMMOUDEH, Thar BAKER, Sohail JABBAR, Shehzad KHALID and Sardar JAF. BotDet: A System for Real Time Botnet Command and Control Traffic Detection. IEEE Access. USA: IEEE Xplore Digital Library, vol. 6, June, p. 38947-38958. ISSN 2169-3536. 2018.
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Basic information
Original name BotDet: A System for Real Time Botnet Command and Control Traffic Detection
Name in Czech BotDet: Systém pro detekci aktivit botnetu v reálném čase
Authors GHAFIR, Ibrahim, Václav PŘENOSIL, Mohammad HAMMOUDEH, Thar BAKER, Sohail JABBAR, Shehzad KHALID and Sardar JAF.
Edition IEEE Access, USA, IEEE Xplore Digital Library, 2018, 2169-3536.
Other information
Original language English
Type of outcome Article in a journal
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
Organization Fakulta informatiky – Repository – Repository
UT WoS 000440397400001
Keywords (in Czech) bezpečnost kritická infrastruktury; cyber útoky ve zdravotnictví; malware; botnet; příkazový a řídící server; systém detekce narušení; korelace výstrah
Keywords in English critical infrastructure security; healthcare cyber attacks; malware; botnet; command and control server; intrusion detection system; alert correlation
Links OFMASUN201301, research and development project.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 30/4/2019 00:57.
Abstract
amp;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.
Abstract (in Czech)
amp;C) - jsou zde popsány čtyři námi vyvinuté metody; (ii) návrh korelačního rámce pro snížení frekvence falešných poplachů generovaných jednotlivými detekčními moduly. Výsledky experimentů dokládají, že procentní úspěšnost systému BotDet činí 82,3% správných detekcí ku 13,6% falešných detekcí. Experimenty dále dokládají, že systém BotDet dokáže pracovat v reálném čase.
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