Advances in Engineering Innovation

Advances in Engineering Innovation

Vol. 3, 23 October 2023


Open Access | Article

AI-driven cybersecurity: Utilizing machine learning and deep learning techniques for real-time threat detection, analysis, and mitigation in complex IT networks

Dabi Dabouabi Dalo Alionsi * 1
1 University of South Florida

* Author to whom correspondence should be addressed.

Advances in Engineering Innovation, Vol. 3 Advances in Engineering Innovation,
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Dabi Dabouabi Dalo Alionsi. AI-driven cybersecurity: Utilizing machine learning and deep learning techniques for real-time threat detection, analysis, and mitigation in complex IT networks. AEI (2023) Vol. 3. DOI: 10.54254/2977-3903/3/2023036.

Abstract

With the escalating complexity of IT networks and the surge in cyber threats, the need for advanced, real-time security solutions has never been more paramount. Machine learning (ML) and deep learning (DL) present promising avenues for enhancing the detection, analysis, and mitigation of threats in these intricate networks. The paper delves into the confluence of ML and DL techniques in the realm of cybersecurity, focusing on their application for real-time threat detection within IT infrastructures. Drawing from recent research and developments, the study underscores the potential of these techniques in outmaneuvering conventional security models, while also shedding light on the inherent challenges and areas for future exploration.

Keywords

machine learning, deep learning, real-time threat detection, IT network security, cybersecurity

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
ISBN (Print)
ISBN (Online)
Published Date
23 October 2023
Series
Advances in Engineering Innovation
ISSN (Print)
2977-3903
ISSN (Online)
2977-3911
DOI
10.54254/2977-3903/3/2023036
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated