Applied Machine Learning for Information Security-Reference-Cited by-同舟云学术

Applied Machine Learning for Information Security

Published:2024-03-31 Issue:1 Volume:5 Page:1-5
ISSN:2692-1626
Container-title:Digital Threats: Research and Practice
language:en
Short-container-title:Digital Threats

Author:

Samtani Sagar¹^ORCID,Raff Edward²^ORCID,Anderson Hyrum³^ORCID

Affiliation:

1. Indiana University, Bloomington, USA

2. Booz Allen Hamilton, University of Maryland, Baltimore County, Baltimore, USA

3. Robust Intelligence, Boise, USA

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

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