Malware Detection by Eating a Whole EXE

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dc.contributor.authorRaff, Edward
dc.contributor.authorBarker, Jon
dc.contributor.authorSylvester, Jared
dc.contributor.authorBrandon, Robert
dc.contributor.authorCatanzaro, Bryan
dc.contributor.authorNicholas, Charles
dc.date.accessioned2019-02-06T15:38:08Z
dc.date.available2019-02-06T15:38:08Z
dc.descriptionThe Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence
dc.description.abstractIn this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a problem presents a number of interesting challenges that have not occurred in tasks such as image processing or NLP. In particular, we note that detection from raw bytes presents a sequence problem with over two million time steps and a problem where batch normalization appear to hinder the learning process. We present our initial work in building a solution to tackle this problem, which has linear complexity dependence on the sequence length, and allows for interpretable sub-regions of the binary to be identified. In doing so we will discuss the many challenges in building a neural network to process data at this scale, and the methods we used to work around them.en_US
dc.description.urihttps://aaai.org/ocs/index.php/WS/AAAIW18/paper/viewFile/16422/15577en_US
dc.format.extent9 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2rt7w-bkok
dc.identifier.citationEdward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, Charles Nicholas, Malware Detection by Eating a Whole EXE, The Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018,https://aaai.org/ocs/index.php/WS/AAAIW18/paper/viewFile/16422/15577en_US
dc.identifier.urihttp://hdl.handle.net/11603/12714
dc.language.isoen_USen_US
dc.publisherAAAIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectmalware detectionen_US
dc.subjectimage processingen_US
dc.subjectvirtual machineen_US
dc.titleMalware Detection by Eating a Whole EXEen_US
dc.typeTexten_US

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