Applied Machine Learning for Information Security

Information security has undoubtedly become a critical aspect of modern cybersecurity practices. Over the past 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 secu...

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Veröffentlicht in:Digital threats (Print) 2024-04, Vol.5 (1), p.1-5, Article 1
Hauptverfasser: Samtani, Sagar, Raff, Edward, Anderson, Hyrum
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creator Samtani, Sagar
Raff, Edward
Anderson, Hyrum
description Information security has undoubtedly become a critical aspect of modern cybersecurity practices. Over the past 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.
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subjects Artificial intelligence
Computing methodologies
Security and privacy
Software and application security
title Applied Machine Learning for Information Security
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