Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review

December 24, 2024 ยท The Cartographer ยท ๐Ÿ› arXiv.org

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
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"Title-pattern auto-detect: Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review"

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Authors Jaouhar Fattahi arXiv ID 2501.03250 Category cs.CR: Cryptography & Security Cross-listed cs.AI Citations 11 Venue arXiv.org Last Checked 3 days ago
Abstract
In the paced realms of cybersecurity and digital forensics machine learning (ML) and deep learning (DL) have emerged as game changing technologies that introduce methods to identify stop and analyze cyber risks. This review presents an overview of the ML and DL approaches used in these fields showcasing their advantages drawbacks and possibilities. It covers a range of AI techniques used in spotting intrusions in systems and classifying malware to prevent cybersecurity attacks, detect anomalies and enhance resilience. This study concludes by highlighting areas where further research is needed and suggesting ways to create transparent and scalable ML and DL solutions that are suited to the evolving landscape of cybersecurity and digital forensics.
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