%0 Conference Paper
%F Oral 
%T Hybrid Intrusion Detection in Information Systems
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%A Pierrot, David
%A Harbi, Nouria
%A Darmont, Jérôme
%< avec comité de lecture
%B 3rd International Conference on Information Science and Security (ICISS 2016)
%C Pattaya, Thailand
%I iCatse
%S Proceedings of the 3rd International Conference on Information Science and Security (ICISS 2016)
%8 2016-12-19
%D 2016
%K Intrusion
%K Detection
%K Firewall
%K Security
%Z Computer Science [cs]/Databases [cs.DB]
%Z Computer Science [cs]/Cryptography and Security [cs.CR]Conference papers
%X The expansion and democratization of the digital world coupled with the effect of the Internet globalization, has allowed individuals, countries, states and companies to interconnect and interact at incidence levels never previously imagined. Cybercrime, in turn, is unfortunately one the negative aspects of this rapid global interconnection expansion. We often find malicious individuals and/or groups aiming to undermine the integrity of Information Systems for either financial gain or to serve a cause. Our study investigates and proposes a hybrid data mining methodology in order to detect abnormal behavior that could potentially threaten the security of an Information System, in a simple way that is understandable to all involved parties, whether they are security experts or standard users.
%G English
%2 https://hal.science/hal-01380026/document
%2 https://hal.science/hal-01380026/file/ocatse.pdf
%L hal-01380026
%U https://hal.science/hal-01380026
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ ERIC
%~ LABEXIMU
%~ LYON2
%~ UDL
%~ UNIV-LYON