Article (Scientific journals)
A training-resistant anomaly detection system
Muller, Steve; Lancrenon, Jean; Harpes, Carlo et al.
2018In Computers and Security, 76, p. 1-11
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Keywords :
Anomaly detection; Intrusion detection system; Machine learning; Network security; Training attack; Artificial intelligence; Computer crime; Denial-of-service attack; Learning systems; Mercury (metal); Telecommunication traffic; Anomaly detection systems; Denial of Service; Detection scheme; Intrusion Detection Systems; Learning process; Network intrusion detection systems; Traffic anomalies; Intrusion detection
Abstract :
[en] Modern network intrusion detection systems rely on machine learning techniques to detect traffic anomalies and thus intruders. However, the ability to learn the network behaviour in real-time comes at a cost: malicious software can interfere with the learning process, and teach the intrusion detection system to accept dangerous traffic. This paper presents an intrusion detection system (IDS) that is able to detect common network attacks including but not limited to, denial-of-service, bot nets, intrusions, and network scans. With the help of the proposed example IDS, we show to what extent the training attack (and more sophisticated variants of it) has an impact on machine learning based detection schemes, and how it can be detected. © 2018 Elsevier Ltd
Disciplines :
Computer science
Identifiers :
eid=2-s2.0-85043535080
Author, co-author :
Muller, Steve ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC)
Lancrenon, Jean;  itrust consulting s.à r.l., Niederanven, Luxembourg
Harpes, Carlo;  itrust consulting s.à r.l., Niederanven, Luxembourg
Le Traon, Yves ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Gombault, Sylvain;  IMT Atlantique, IRISA, UBL, Rennes, Bretagne, France
Bonnin, Jean-Marie;  IMT Atlantique, IRISA, UBL, Rennes, Bretagne, France
External co-authors :
yes
Title :
A training-resistant anomaly detection system
Publication date :
2018
Journal title :
Computers and Security
ISSN :
0167-4048
Publisher :
Elsevier Ltd
Volume :
76
Pages :
1-11
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 13 July 2018

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