Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review
It provides a comprehensive overview for researchers and practitioners in cybersecurity and digital forensics, but is incremental as it synthesizes existing work without introducing new methods or data.
This review paper surveys machine learning and deep learning techniques applied in cybersecurity and digital forensics, covering methods for intrusion detection, malware classification, and anomaly prevention, and concludes by identifying research gaps and suggesting future directions for transparent and scalable solutions.
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.