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

Jaouhar Fattahi

TL;DR

An overview of the ML and DL approaches used in these fields showcasing their advantages drawbacks and possibilities is presented and ways to create transparent and scalable ML and DL solutions that are suited to the evolving landscape of cybersecurity and digital forensics are suggested.

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.

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

TL;DR

An overview of the ML and DL approaches used in these fields showcasing their advantages drawbacks and possibilities is presented and ways to create transparent and scalable ML and DL solutions that are suited to the evolving landscape of cybersecurity and digital forensics are suggested.

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.
Paper Structure (70 sections, 74 equations, 18 figures, 9 tables)

This paper contains 70 sections, 74 equations, 18 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Standard Machine Learning techniques and algorithms
  3. Machine Learning
  4. Supervised learning
  5. Unsupervised learning
  6. Classification
  7. Regression
  8. Clustering
  9. Reinforcement learning
  10. Common problems
  11. Machine Learning Algorithms
  12. Linear Regressions
  13. Logistic Regressions
  14. Decision Trees
  15. Random Forest
  16. ...and 55 more sections

Figures (18)

  • Figure 2: Linear Regression
  • Figure 3: Logistic Regression
  • Figure 4: Linear Regression versus Logistic Regression
  • Figure 5: Decision tree process
  • Figure 6: Support Vector Machine (in two dimensions)
  • ...and 13 more figures