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dc.contributor.authorNarang, Pratik
dc.contributor.authorHota, Chittaranjan
dc.contributor.authorSencar, Hüsrev Taha
dc.date.accessioned2019-06-26T07:40:32Z
dc.date.available2019-06-26T07:40:32Z
dc.date.issued2016-12-15
dc.identifier.citationNarang, P., Hota, C., & Sencar, H. T. (2016). Noise-resistant mechanisms for the detection of stealthy peer-to-peer botnets. Computer Communications, 96, 29-42.en_US
dc.identifier.issn0140-3664
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0140366416302341?via%3Dihub
dc.identifier.urihttp://hdl.handle.net/20.500.11851/1134
dc.description.abstractThe problem of detection of malicious network traffic is adversarial in nature. Accurate detection of stealthy Peer-to-Peer botnets is an ongoing research problem. Past research on detection of P2P botnets has frequently used machine learning algorithms to build detection models. However, most prior work lacks the evaluation of such detection models in the presence of deliberate injection of noise by an adversary. Furthermore, detection of P2P botnets in the presence of benign P2P traffic has received little attention from the research community. This work proposes a novel approach for the detection of stealthy P2P botnets (in presence of benign P2P traffic) using conversation-based mechanisms and new features based on Fourier transforms and information entropy. We use real-world botnet data to compare the performance of our features with traditional 'flow-based' features employed by past research, and demonstrate that our approach is more resilient towards the injection of noise in the communication patterns by an adversary. We build detection models with multiple supervised machine learning algorithms. With our approach, we could detect P2P botnet traffic in the presence of injected noise with True Positive rate as high as 90%. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBotneten_US
dc.subjectMachine Learningen_US
dc.subjectPeer-To-Peeren_US
dc.subjectIntrusion Detectionen_US
dc.subjectSecurityen_US
dc.titleNoise-resistant mechanisms for the detection of stealthy peer-to-peer botnetsen_US
dc.typearticleen_US
dc.relation.journalComputer Communicationsen_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Computer Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume96
dc.identifier.startpage29
dc.identifier.endpage42
dc.contributor.orcidhttps://orcid.org/0000-0001-6910-6194
dc.identifier.wosWOS:000389163700003
dc.identifier.scopus2-s2.0-84971602490
dc.contributor.tobbetuauthorSencar, Hüsrev Taha
dc.contributor.YOKid143687
dc.identifier.doi10.1016/j.comcom.2016.05.017
dc.contributor.ScopusAuthorID8616233200
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR
dc.relation.otherDepartment of Information Technology, Govt. of India, New Delhi, India [12(13)/2012-ESD]en_US


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