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A study on the performances of danger theory and negative selection algorithms for mobile spam detection

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dc.FundingDetailsMinistry of Higher Education, Malaysia,�MOHE: USIM/FRGS/FST/30/50315
dc.FundingDetailsAuthors wish to thank Saidatul Syafiqah and Nur Amirah for the help with experiments setup and simulation. This work is funded by the grant of the Ministry of Higher Education Malaysia (MOHE) with the code of FRGS USIM/FRGS/FST/30/50315.
dc.citedby1
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorSulaiman N.F.en_US
dc.contributor.authorJali M.Z.en_US
dc.contributor.authorAbdullah Z.H.en_US
dc.contributor.authorIsmail S.en_US
dc.date.accessioned2024-05-28T08:28:05Z
dc.date.available2024-05-28T08:28:05Z
dc.date.issued2017
dc.description.abstractSpamming activities using text messages on mobile phone are widely spreading, as in line with the development of technology for mobile phones. This phenomenon has contributed a major threat that impacts the usability of messages. Even though many techniques have been proposed and introduced for detecting these ‘unwanted’ messages, all those efforts still cannot bring this problem to an end. The major challenges in detecting and filtering spam messages today are ineffective solution to deal with strains of spam messages because of the variety content of messages and the attitude of users themselves. This paper aims to view the performance of Artificial Immune System (AIS) algorithms inspired from the ideology of Biology Immune System (BIS) in human body for detecting spam messages on mobile phone. Two types of AIS algorithms were used; Danger Theory (DT) and Negative Selection (NS). Their performances were measured and compared in terms of effectiveness, efficiency and Receiver Over Characteristic (ROC) area, tested on WEKA using three different datasets. From our conduction of experiments, generic Negative Selection algorithm performs betteen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1166/asl.2017.8887
dc.identifier.epage4590
dc.identifier.issn19366612
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85023739672
dc.identifier.spage4586
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85023739672&doi=10.1166%2fasl.2017.8887&partnerID=40&md5=63b1d4a8d0c3e1ec3a838dacfa471cfd
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8836
dc.identifier.volume23
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherAmerican Scientific Publishersen_US
dc.relation.ispartofAdvanced Science Lettersen_US
dc.sourceScopus
dc.subjectArtificial immune systemen_US
dc.subjectDanger theoryen_US
dc.subjectDetectionen_US
dc.subjectMobile spamen_US
dc.subjectNegative selectionen_US
dc.titleA study on the performances of danger theory and negative selection algorithms for mobile spam detectionen_US
dc.typeArticleen_US
dspace.entity.typePublication

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