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dc.contributor.authorBouyeddou, Benamar
dc.contributor.authorKadri, Benamar
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.date.accessioned2021-02-25T07:10:38Z
dc.date.available2021-02-25T07:10:38Z
dc.date.issued2020-10-26
dc.identifier.citationBouyeddou, B., Kadri, B., Harrou, F., & Sun, Y. (2020). Nonparametric Kullback-Leibler distance-based method for networks intrusion detection. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI). doi:10.1109/icdabi51230.2020.9325642
dc.identifier.isbn9781728196756
dc.identifier.doi10.1109/icdabi51230.2020.9325642
dc.identifier.urihttp://hdl.handle.net/10754/667671
dc.description.abstractAnomaly detection enables identifying atypical events in network systems. Revealing denial of service (DOS) and distributed DOS (DDOS) is a critical security challenge confronting network technologies. This work advocates using Kullback-Leibler distance (KLD) to track DOS and DDOS flooding attacks, including SYN flood, UDP flood, and Smurf attacks. The proposed mechanism's key novelty is the amalgamation of the desirable characteristics of KLD with the sensitivity of an exponential smoothing algorithm. Notably, the use of exponentially smoothing is expected to improve the detector sensitivity to small anomalies. Besides, the proposed mechanism does not need knowledge about data distribution. Meanwhile, kernel density estimation usage to set a threshold for ES-KLD decision statistic improves the flexibility of the proposed mechanism. Tests on the publicly available DARPA99 dataset showing enhanced outputs of the developed approach in detecting cyber-attacks compared to other traditional monitoring procedures.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9325642/
dc.rightsArchived with thanks to IEEE
dc.titleNonparametric Kullback-Leibler distance-based method for networks intrusion detection
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.conference.date2020-10-26 to 2020-10-27
dc.conference.name2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020
dc.conference.locationSakheer, BHR
dc.eprint.versionPre-print
dc.contributor.institutionAbou Bekr Belkaid University,STIC Lab.,Department of Telecommunications,Tlemcen,Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
dc.identifier.eid2-s2.0-85100506602
refterms.dateFOA2021-02-25T12:06:53Z


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