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dc.contributor.authorMira, Fahad
dc.contributor.authorHuang, Wei
dc.contributor.authorBrown, Antony
dc.date.accessioned2020-08-04T11:28:56Z
dc.date.available2020-08-04T11:28:56Z
dc.date.issued2017-10-26
dc.identifier.citationMira F, Huang W, Brown A (2017) 'Improving malware detection time by using RLE and N-gram', 23rd International Conference on Automation and Computing (ICAC) - Huddersfield, Institute of Electrical and Electronics Engineers Inc..en_US
dc.identifier.isbn9780701702618
dc.identifier.doi10.23919/IConAC.2017.8082001
dc.identifier.urihttp://hdl.handle.net/10547/624314
dc.description.abstractMalware is a widespread problem and despite the common use of anti-virus software, the diversity of malware is still increasing. A major challenge facing the anti-virus industry is how to effectively detect thousands of malware samples that are received every day. In this paper, a novel approach based Run Length Encoding (RLE) algorithm and n-gram are proposed to improve malware detect on dynamic analysis of based on API sequences.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/8082001en_US
dc.subjectmalwareen_US
dc.subjectAPI call sequencesen_US
dc.subjectdetection timeen_US
dc.subjectN-gramen_US
dc.subjectRLEen_US
dc.titleImproving malware detection time by using RLE and N-gramen_US
dc.typeConference papers, meetings and proceedingsen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.date.updated2020-08-04T11:26:45Z
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