Would a File by Any Other Name Seem as Malicious?

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dc.contributor.authorNguyen, Andre T.
dc.contributor.authorRaff, Edward
dc.contributor.authorSant-Miller, Aaron
dc.date.accessioned2020-03-11T17:55:13Z
dc.date.available2020-03-11T17:55:13Z
dc.date.issued2019-10-10
dc.description.abstractSuccessful malware attacks on information technology systems can cause millions of dollars in damage, the exposure of sensitive and private information, and the irreversible destruction of data. Anti-virus systems that analyze a file's contents use a combination of static and dynamic analysis to detect and remove/remediate such malware. However, examining a file's entire contents is not always possible in practice, as the volume and velocity of incoming data may be too high, or access to the underlying file contents may be restricted or unavailable. If it were possible to obtain estimates of a file's relative likelihood of being malicious without looking at the file contents, we could better prioritize file processing order and aid analysts in situations where a file is unavailable. In this work, we demonstrate that file names can contain information predictive of the presence of malware in a file. In particular, we show the effectiveness of a character-level convolutional neural network at predicting malware status using file names on Endgame's EMBER malware detection benchmark dataset.en_US
dc.description.urihttps://arxiv.org/abs/1910.04753en_US
dc.format.extent10 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m2okq6-ikow
dc.identifier.citationNguyen, Andre T.; Raff, Edward; Sant-Miller, Aaron; Would a File by Any Other Name Seem as Malicious?; Cryptography and Security (2019); https://arxiv.org/abs/1910.04753en_US
dc.identifier.urihttp://hdl.handle.net/11603/17551
dc.language.isoen_USen_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.subjectinformation technology systemsen_US
dc.subjectmalware attacksen_US
dc.subjectanti-virus systemsen_US
dc.subjectcharacter-level convolutional neural networken_US
dc.titleWould a File by Any Other Name Seem as Malicious?en_US
dc.typeTexten_US

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