AI-driven threat intelligence for real-time cybersecurity: Frameworks, tools, and future directions
1 Computer Science, The University of Alabama, Tuscaloosa, Alabama, USA.
2 Computer Science, Maharishi International University (MIU), Fairfield, IA, USA.
3 Education Policy Organization and Leadership, University of Illinois, Urbana Champaign, IL, USA.
4 Information Security and Digital Forensics, University of East London, UK.
Review
Open Access Research Journal of Science and Technology, 2024, 12(02), 040–048.
Article DOI: 10.53022/oarjst.2024.12.2.0135
Publication history:
Received on 28 September 2024; revised on 06 November 2024; accepted on 09 November 2024
Abstract:
AI-driven threat intelligence is transforming cybersecurity by enhancing real-time threat detection, analysis, and response capabilities. This paper reviews state-of-the-art AI frameworks, machine learning models, and tools that support threat intelligence, providing a survey of current research in the field and identifying challenges and future directions for real-time cybersecurity. Techniques such as supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP) contribute to the robustness of threat detection, while evolving frameworks and ethics guide AI implementation in security operations. By addressing the increasing sophistication of cyber threats, AI-driven approaches aim to create a proactive, dynamic cybersecurity posture that can keep up with evolving cyber adversaries.
Keywords:
Artificial Intelligence; Cybersecurity; Machine Learning; Deep Learning
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0