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Revolutionizing Cyber Security: Exploring the Synergy of Machine Learning and Logical Reasoning for Cyber Threats and Mitigation
Center for C2PS and Department of EECS, Khalifa University, Abu Dhabi (ARE).
Department of Computer Science and Engineering, University of North Texas, Denton, Texas (USA).
University West, Department of Engineering Science, Division of computer engineering and computer science. Department of Electrical Engineering, University of Cape Town, Cape Town (ZAF).
Center for C2PS and Department of EECS, Khalifa University, Abu Dhabi (ARE).
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2023 (English)In: 2023 IEEE Computer Society Annual Symposium on VLSI (ISVLSI): 20-23 June 2023, IEEE Computer Society, 2023, Vol. 2023-JuneConference paper, Published paper (Refereed)
Abstract [en]

The integration of machine learning (ML) and logical reasoning (LR) in cyber security is an emerging field that shows great potential for improving the efficiency and effectiveness of security systems. While ML can detect anomalies and patterns in large amounts of data, LR can provide a higher-level understanding of threats and enable better decision-making. This paper explores the future of ML and LR in cyber security and highlights how the integration of these two approaches can lead to more robust security systems. We discuss several use cases that demonstrate the effectiveness of the integrated approach, such as threat detection and response, vulnerability assessment, and security policy enforcement. Finally, we identify several research directions that will help advance the field, including the development of more explainable ML models and the integration of human-in-the-loop approaches. © 2023 IEEE.

Place, publisher, year, edition, pages
IEEE Computer Society, 2023. Vol. 2023-June
Keywords [en]
Decision making; Integration; Machine learning; Security systems; Cyber security; Logical reasoning; Machine-learning; Synergy of machine learning and logical reasoning; Synergy of machine learning and logical reasoning for cybe security; Cybersecurity
National Category
Information Systems, Social aspects Computer Systems
Identifiers
URN: urn:nbn:se:hv:diva-21182DOI: 10.1109/ISVLSI59464.2023.10238483ISI: 001066014800014Scopus ID: 2-s2.0-85172145760ISBN: 979-8-3503-2769-4 (electronic)ISBN: 979-8-3503-2770-0 (print)OAI: oai:DiVA.org:hv-21182DiVA, id: diva2:1829600
Conference
2023 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Foz do Iguacu, Brazil, 2023
Available from: 2024-01-19 Created: 2024-01-19 Last updated: 2024-01-19Bibliographically approved

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