Applied Machine Learning for Information Security

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dc.contributor.authorSamtani, Sagar
dc.contributor.authorRaff, Edward
dc.contributor.authorAnderson, Hyrum
dc.date.accessioned2024-03-27T13:26:14Z
dc.date.available2024-03-27T13:26:14Z
dc.date.issued2024-03-11
dc.description.abstractInformation security has undoubtedly become a critical aspect of modern cybersecurity practices. Over the last half-decade, numerous academic and industry groups have sought to develop machine learning, deep learning, and other areas of artificial intelligence-enabled analytics into information security practices. The Conference on Applied Machine Learning (CAMLIS) is an emerging venue that seeks to gather researchers and practitioners to discuss applied and fundamental research on machine learning for information security applications. In 2021, CAMLIS partnered with ACM Digital Threats: Research and Practice (DTRAP) to provide opportunities for authors of accepted CAMLIS papers to submit their research for consideration into ACM DTRAP via a Special Issue on Applied Machine Learning for Information Security. This editorial summarizes the results of this Special Issue.
dc.description.urihttps://dl.acm.org/doi/10.1145/3652029
dc.format.extent5 pages
dc.genrejournal articles
dc.genrepostprints
dc.identifierdoi:10.13016/m2rode-z8ew
dc.identifier.citationSamtani, Sagar, Edward Raff, and Hyrum Anderson. “Applied Machine Learning for Information Security.” Digital Threats: Research and Practice, March 11, 2024. https://doi.org/10.1145/3652029.
dc.identifier.urihttps://doi.org/10.1145/3652029
dc.identifier.urihttp://hdl.handle.net/11603/32684
dc.language.isoen_US
dc.publisherACM
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Data Science
dc.subjectapplied machine learning
dc.subjectartificial intelligence
dc.subjectcybersecurity
dc.subjectdeep learning
dc.subjectinformation security
dc.titleApplied Machine Learning for Information Security
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-9900-1972

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