Integrated soft computing for intrusion detection on computer network security

dc.contributor.authorSirikanjana Pilabutr
dc.contributor.authorPreecha Somwang
dc.contributor.authorSurat Srinoy
dc.contributor.correspondenceS. Pilabutr; Faculty of Information Sciences, Nakhon Ratchasima College, Nakhon Ratchasima, Thailand; email: sirikanjana@nmc.ac.th
dc.date.accessioned2025-03-10T07:37:41Z
dc.date.available2025-03-10T07:37:41Z
dc.date.issued2011
dc.description.abstractComputer network security is very important for all business sectors. The Intrusion Detection Systems (IDS) is one technique that prevents an information system from a computer networks attacker. The IDS is able to detect behavior of new attacker which is indicated both correct Detection Rate and False Alarm Rate. This paper presents the new intrusion detection technique that applied hybrid of unsupervised/supervised learning scheme. To combine between the Independent Component Analysis (ICA) and the Support Vector Machine (SVM) are the advantage of these new IDS. The benefit of the ICA is to separate these independent components from the monitored variables. And the SVM is able to classify a different groups of data such as normal or anomalous. As a result, the new IDS are able to improve the performance of anomaly intrusion detection and intrusion detection. © 2011 IEEE.
dc.identifier.citationICCAIE 2011 - 2011 IEEE Conference on Computer Applications and Industrial Electronics
dc.identifier.doi10.1109/ICCAIE.2011.6162197
dc.identifier.isbn978-145772058-1
dc.identifier.scopus2-s2.0-84858773477
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5030
dc.languageEnglish
dc.rights.holderScopus
dc.subjectIndependent Component Analysis
dc.subjectIntrusion Detection System
dc.subjectNetwork Security
dc.subjectSupport Vector Machine
dc.titleIntegrated soft computing for intrusion detection on computer network security
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84858773477&doi=10.1109%2fICCAIE.2011.6162197&partnerID=40&md5=1df61c7893b20269664cd602db1e2184
oaire.citation.endPage563
oaire.citation.startPage559
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