Applied Machine Learning for Information Security

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https://dl.acm.org/doi/10.1145/3652029

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https://doi.org/10.1145/3652029
 http://hdl.handle.net/11603/32684

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UMBC Faculty Collection
UMBC Data Science

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https://orcid.org/0000-0002-9900-1972

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2024-03-11

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journal articles
postprints
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Citation of Original Publication

Samtani, 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.

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Subjects

applied machine learning
artificial intelligence
cybersecurity
deep learning
information security

Abstract

Information 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.