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Signature-based malware detection using sequences of N-grams

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dc.FundingDetailsMinistry of Higher Education, Malaysia: FRGS/1/2015/ICT01/USIM/02/1
dc.FundingDetailsThis paper is supported by a research project funded under The Ministry of Higher Education, Malaysia (Grant No FRGS/1/2015/ICT01/USIM/02/1).
dc.contributor.affiliationsIslamic Science Institute
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorAbiola A.M.en_US
dc.contributor.authorMarhusin M.F.en_US
dc.date.accessioned2024-05-28T08:35:57Z
dc.date.available2024-05-28T08:35:57Z
dc.date.issued2018
dc.description.abstractThe focus of our study is on one set of malware family known as Brontok worms. These worms have long been a huge burden to most Windows-based user platforms. A prototype of the antivirus was able to scan files and accurately detect any traces of the Brontok malware signatures in the scanned files. In this study, we developed a detection model by extracting the signatures of the Brontok worms and used an n-gram technique to break down the signatures. This process makes the task to remove redundancies between the signatures of the different types of Brontok malware easier. Hence, it was used in this study to accurately differentiate between the signatures of both malicious and normal files. During the experiment, we have successfully detected the presence of Brontok worms while correctly identifying the benign ones. The techniques employed in the experiment provided some insight on creating a good signature-based detector, which could be used to create a more credible solution that eliminates any threats of old malware that may resurface in the future.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14419/ijet.v7i4.15.21432
dc.identifier.epage125
dc.identifier.issn2227524X
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85054691476
dc.identifier.spage120
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85054691476&doi=10.14419%2fijet.v7i4.15.21432&partnerID=40&md5=c53529e218f5e8e9ffa6ffde11c13f3e
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9141
dc.identifier.volume7
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherScience Publishing Corporation Incen_US
dc.relation.ispartofOpen Accessen_US
dc.relation.ispartofInternational Journal of Engineering and Technology(UAE)
dc.sourceScopus
dc.subjectK-gramsen_US
dc.subjectN-gramsen_US
dc.subjectSignature-based detectionen_US
dc.titleSignature-based malware detection using sequences of N-gramsen_US
dc.title.alternativeInt. J. Eng. Technol.en_US
dc.typeArticleen_US
dspace.entity.typePublication

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