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

TL;DR

This review discusses how machine learning and deep learning techniques are applied in cybersecurity and digital forensics to detect threats, classify malware, and improve system resilience, while highlighting challenges and future research directions.

Contribution

It provides a comprehensive overview of ML and DL methods in cybersecurity and digital forensics, emphasizing their advantages, limitations, and potential for future development.

Findings

ML and DL improve threat detection accuracy

Techniques vary in effectiveness and scalability

Identifies gaps and future research needs

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.