Novel malware detection methods by using LCS and LCSS

2.50
Hdl Handle:
http://hdl.handle.net/10547/622051
Title:
Novel malware detection methods by using LCS and LCSS
Authors:
Mira, Fahad; Brown, Antony; Huang, Wei
Other Titles:
Proceedings of The 22nd IEEE International Conference on Automation & Computing
Abstract:
The field of computer security faces numerous vulnerabilities which cause network resources to become unavailable and violate systems confidentiality and integrity. Malicious software (Malware) has become one of the most serious security threats on the Internet. Malware 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 on dynamic analysis of malware is proposed whereby Longest Common Subsequence (LCSS) and Longest Common Substring (LCS) algorithms are adopted to accurately detect malware. The empirical results show that the proposed approach performs favorably compared to other related work that use API call sequences.
Affiliation:
University of Bedfordshire
Citation:
Mira F., Brown A., Huang W. (2016) 'Novel malware detection methods by using LCS and LCSS', The 22nd IEEE International Conference on Automation & Computing - Colchester, Institute of Electrical and Electronics Engineers Inc..
Publisher:
Institute of Electrical and Electronics Engineers Inc.
Issue Date:
24-Oct-2016
URI:
http://hdl.handle.net/10547/622051
DOI:
10.1109/IConAC.2016.7604978
Additional Links:
http://ieeexplore.ieee.org/document/7604978/
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9781862181311
Appears in Collections:
Computing

Full metadata record

DC FieldValue Language
dc.contributor.authorMira, Fahaden
dc.contributor.authorBrown, Antonyen
dc.contributor.authorHuang, Weien
dc.date.accessioned2017-03-14T11:23:46Z-
dc.date.available2017-03-14T11:23:46Z-
dc.date.issued2016-10-24-
dc.identifier.citationMira F., Brown A., Huang W. (2016) 'Novel malware detection methods by using LCS and LCSS', The 22nd IEEE International Conference on Automation & Computing - Colchester, Institute of Electrical and Electronics Engineers Inc..en
dc.identifier.isbn9781862181311-
dc.identifier.doi10.1109/IConAC.2016.7604978-
dc.identifier.urihttp://hdl.handle.net/10547/622051-
dc.description.abstractThe field of computer security faces numerous vulnerabilities which cause network resources to become unavailable and violate systems confidentiality and integrity. Malicious software (Malware) has become one of the most serious security threats on the Internet. Malware 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 on dynamic analysis of malware is proposed whereby Longest Common Subsequence (LCSS) and Longest Common Substring (LCS) algorithms are adopted to accurately detect malware. The empirical results show that the proposed approach performs favorably compared to other related work that use API call sequences.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7604978/en
dc.subjectdetectionen
dc.subjectAPI call sequencesen
dc.subjectmalwareen
dc.subjectLCSSen
dc.subjectLCSen
dc.subjectcomputer securityen
dc.titleNovel malware detection methods by using LCS and LCSSen
dc.title.alternativeProceedings of The 22nd IEEE International Conference on Automation & Computingen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
dc.date.updated2017-03-14T11:18:50Z-
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