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dc.contributor.advisorAwan, Irfan U.
dc.contributor.advisorDisso, Jules P.
dc.contributor.advisorCullen, Andrea J.
dc.contributor.authorNamanya, Anitta P.
dc.date.accessioned2018-05-14T15:04:40Z
dc.date.available2018-05-14T15:04:40Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10454/15863
dc.description.abstractMalware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. This thesis proposes advancing automation of the malware static analysis and detection to improve the decision-making confidence levels of a standard computer user in regards to a file’s malicious status. Therefore, this work introduces a framework that relies on two novel approaches to score the malicious intent of a file. The first approach attaches a probabilistic score to heuristic anomalies to calculate an overall file malicious score while the second approach uses fuzzy hashes and evidence combination theory for more efficient malware detection. The approaches’ resultant quantifiable scores measure the malicious intent of the file. The designed schemes were validated using a dataset of “clean” and “malicious” files. The results obtained show that the framework achieves true positive – false positive detection rate “trade-offs” for efficient malware detection.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectMalware detectionen_US
dc.subjectEvidence combinational theoryen_US
dc.subjectHashesen_US
dc.subjectFile featuresen_US
dc.subjectAnomaliesen_US
dc.subjectProbabilistic scoringen_US
dc.subjectAutomated static analysesen_US
dc.subjectMalwareen_US
dc.subjectPortable executable (PE)en_US
dc.titleA Heuristic Featured Based Quantification Framework for Efficient Malware Detection. Measuring the Malicious intent of a file using anomaly probabilistic scoring and evidence combinational theory with fuzzy hashing for malware detection in Portable Executable filesen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentSchool of Electrical Engineering and Computer Scienceen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2016
refterms.dateFOA2018-07-29T01:43:19Z


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