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dc.contributor.authorBouyeddou, Benamar
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.contributor.authorKadri, Benamar
dc.date.accessioned2018-08-27T11:46:18Z
dc.date.available2018-08-27T11:46:18Z
dc.date.issued2018-06-28
dc.identifier.citationBouyeddou B, Harrou F, Sun Y, Kadri B (2018) Detection of smurf flooding attacks using Kullback-Leibler-based scheme. 2018 4th International Conference on Computer and Technology Applications (ICCTA). Available: http://dx.doi.org/10.1109/cata.2018.8398647.
dc.identifier.doi10.1109/cata.2018.8398647
dc.identifier.urihttp://hdl.handle.net/10754/628245
dc.description.abstractReliable and timely detection of cyber attacks become indispensable to protect networks and systems. Internet control message protocol (ICMP) flood attacks are still one of the most challenging threats in both IPv4 and IPv6 networks. This paper proposed an approach based on Kullback-Leibler divergence (KLD) to detect ICMP-based Denial Of service (DOS) and Distributed Denial Of Service (DDOS) flooding attacks. This is motivated by the high capacity of KLD to quantitatively discriminate between two distributions. Here, the three-sigma rule is applied to the KLD distances for anomaly detection. We evaluated the effectiveness of this scheme by using the 1999 DARPA Intrusion Detection Evaluation Datasets.
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors (Benamar Bouyeddou and Benamar Kadri) would like to thank the STIC Lab, Department of Telecommunications, Abou Bekr Belkaid University for the continued support during the research.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8398647/
dc.rightsArchived with thanks to 2018 4th International Conference on Computer and Technology Applications (ICCTA)
dc.subjectICMP flood
dc.subjectcyber-attack
dc.subjectKL distance
dc.subjectanomaly detection
dc.subjectDARPA99 dataset
dc.titleDetection of smurf flooding attacks using Kullback-Leibler-based scheme
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journal2018 4th International Conference on Computer and Technology Applications (ICCTA)
dc.eprint.versionPost-print
dc.contributor.institutionSTIC Lab., Department of Telecommunications, Abou Bekr Belkaid University, Tlemcen, Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2018-09-02T12:04:18Z
dc.date.published-online2018-06-28
dc.date.published-print2018-05


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