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dc.contributor.authorSpencer, H.-
dc.contributor.authorWang, W.-
dc.contributor.authorSun, R.-
dc.contributor.authorXue, M.-
dc.identifier.citationProceedings of the ACM International Conference Proceeding Series (ACSW), 2022, pp.56-64-
dc.description.abstractWith the increasingly rapid development of new malicious computer software by bad faith actors, both commercial and researchoriented antivirus detectors have come to make greater use of machine learning tactics to identify such malware as harmful before end users are exposed to their effects. This, in turn, has spurred the development of tools that allow for known malware to be manipulated such that they can evade being classified as dangerous by these machine learning-based detectors, while retaining their malicious functionality. These manipulations function by applying a set of changes that can be made to Windows programs that result in a different file structure and signature without altering the software’s capabilities. Various proposals have been made for the most effective way of applying these alterations to input malware to deceive static malware detectors; the purpose of this research is to examine these proposals and test their implementations to determine which tactics tend to generate the most successful attacks.-
dc.description.statementofresponsibilityHamish Spencer, Wei Wang, Ruoxi Sun, Minhui Xue-
dc.publisherAssociation for Computing Machinery-
dc.rights© 2022 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
dc.titleDissecting Malware in the Wild-
dc.typeConference paper-
dc.contributor.conferenceAustralasian Computer Science Week (ACSW) (14 Feb 2022 - 17 Feb 2022 : Virtual Online)-
dc.identifier.orcidSun, R. [0000-0001-5404-8550]-
dc.identifier.orcidXue, M. [0000-0001-5411-5039] [0000-0002-9172-4252]-
Appears in Collections:Computer Science publications

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