Publication:
Hybrid of rough set theory and Artificial Immune Recognition System as a solution to decrease false alarm rate in intrusion detection system

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dc.Conferencecode88379
dc.Conferencedate5 December 2011 through 8 December 2011
dc.ConferencelocationMalacca
dc.Conferencename2011 7th International Conference on Information Assurance and Security, IAS 2011
dc.citedby5
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorSabri F.N.M.en_US
dc.contributor.authorNorwawi N.M.en_US
dc.contributor.authorSeman K.en_US
dc.date.accessioned2024-05-28T08:25:16Z
dc.date.available2024-05-28T08:25:16Z
dc.date.issued2011
dc.description.abstractDenial of Service (DoS) attacks is one of the security threats for computer systems and applications. It usually make use of software bugs to crash or freeze a service or network resource or bandwidth limits by making use of a flood attack to saturate all bandwidth. Predicting a potential DOS attacks would be very helpful for an IT departments or managements to optimize the security of intrusion detection system (IDS). Nowadays, false alarm rates and accuracy become the main subject to be addressed in measuring the effectiveness of IDS. Thus, the purpose of this work is to search the classifier that is capable to reduce the false alarm rates and increase the accuracy of the detection system. This study applied Artificial Immune System (AIS) in IDS. However, this study has been improved by using integration of rough set theory (RST) with Artificial Immune Recognition System 1 (AIRS1) algorithm, (Rough-AIRS1) to categorize the DoS samples. RST is expected to be able to reduce the redundant features from huge amount of data that is capable to increase the performance of the classification. Furthermore, AIS is an incremental learning approach that will minimize duplications of cases in a knowledge based. It will be efficient in terms of memory storage and searching for similarities in Intrusion Detection (IDS) attacks patterns. This study use NSL-KDD 20% train dataset to test the classifiers. Then, the performances are compared with single AIRS1 and J48 algorithm. Results from these experiments show that Rough-AIRS1 has lower number of false alarm rate compared to single AIRS but a little bit higher than J48. However, accuracy for this hybrid technique is slightly lower compared to others. � 2011 IEEE.
dc.description.natureFinalen_US
dc.identifier.ArtNo6122808
dc.identifier.doi10.1109/ISIAS.2011.6122808
dc.identifier.epage138
dc.identifier.isbn9781460000000
dc.identifier.scopus2-s2.0-84856660614
dc.identifier.spage134
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84856660614&doi=10.1109%2fISIAS.2011.6122808&partnerID=40&md5=1dc2b0829ee99c7453ded68b0359eff4
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8628
dc.languageEnglish
dc.language.isoen_US
dc.relation.ispartofProceedings of the 2011 7th International Conference on Information Assurance and Security, IAS 2011
dc.sourceScopus
dc.subjectaccuracyen_US
dc.subjectartificial immune recognition systemen_US
dc.subjectfalse alarm rateen_US
dc.subjectIntrusion detection systemen_US
dc.subjectrough set theoryen_US
dc.subjectaccuracyen_US
dc.subjectArtificial immune recognition systemen_US
dc.subjectFalse alarm rateen_US
dc.subjectIntrusion detection systemen_US
dc.subjectRough seten_US
dc.subjectAlarm systemsen_US
dc.subjectAlgorithmsen_US
dc.subjectBandwidthen_US
dc.subjectComputer crimeen_US
dc.subjectErrorsen_US
dc.subjectKnowledge based systemsen_US
dc.subjectNetwork securityen_US
dc.subjectProgram debuggingen_US
dc.subjectRough set theoryen_US
dc.subjectStatistical testsen_US
dc.titleHybrid of rough set theory and Artificial Immune Recognition System as a solution to decrease false alarm rate in intrusion detection system
dc.typeConference Paperen_US
dspace.entity.typePublication

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