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Machine Learning for CybersecurityApplication of Machine Learning (ML) to Address Cybersecurity Threats

Machine Learning for Cybersecurity: Application of Machine Learning (ML) to Address Cybersecurity... [As cybersecurity threats keep growing exponentially in scale, frequency, and impact, legacy-based threat detection systems have proven inadequate. This has prompted the use of machine learning (hereafter, ML) to help address the problem. But as organizations increasingly use intelligent cybersecurity techniques, the overall efficacy and benefit analysis of these ML-based digital security systems remain a subject of increasing scholarly inquiry. The present study seeks to expand and add to this growing body of literature by demonstrating the applications of ML-based data analysis techniques to various problem domains in cybersecurity. To achieve this objective, a rapid evidence assessment (REA) of existing scholarly literature on the subject matter is adopted. The aim is to present a snapshot of the various ways ML is being applied to help address cybersecurity threat challenges.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Machine Learning for CybersecurityApplication of Machine Learning (ML) to Address Cybersecurity Threats

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Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-031-15892-6
Pages
1 –11
DOI
10.1007/978-3-031-15893-3_1
Publisher site
See Chapter on Publisher Site

Abstract

[As cybersecurity threats keep growing exponentially in scale, frequency, and impact, legacy-based threat detection systems have proven inadequate. This has prompted the use of machine learning (hereafter, ML) to help address the problem. But as organizations increasingly use intelligent cybersecurity techniques, the overall efficacy and benefit analysis of these ML-based digital security systems remain a subject of increasing scholarly inquiry. The present study seeks to expand and add to this growing body of literature by demonstrating the applications of ML-based data analysis techniques to various problem domains in cybersecurity. To achieve this objective, a rapid evidence assessment (REA) of existing scholarly literature on the subject matter is adopted. The aim is to present a snapshot of the various ways ML is being applied to help address cybersecurity threat challenges.]

Published: Sep 24, 2022

Keywords: Machine learning security; Deep learning algorithms; AI for cybersecurity; Data analytics and cybersecurity; Cyberattacks; Security threats

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