Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Dec-24-2024
- Country:
- Asia
- Europe
- Germany > North Rhine-Westphalia
- Arnsberg Region > Dortmund (0.04)
- Middle East > Malta (0.04)
- Romania > București - Ilfov Development Region
- Municipality of Bucharest > Bucharest (0.04)
- United Kingdom > England
- Cambridgeshire > Cambridge (0.04)
- Germany > North Rhine-Westphalia
- North America
- Canada
- Mexico > Quintana Roo
- Cancún (0.04)
- United States
- California > San Diego County
- San Diego (0.04)
- New York > New York County
- New York City (0.04)
- Wisconsin (0.04)
- California > San Diego County
- Oceania
- Australia > Australian Capital Territory
- Canberra (0.04)
- New Zealand > North Island
- Waikato (0.04)
- Australia > Australian Capital Territory
- Genre:
- Overview (1.00)
- Research Report (1.00)
- Industry:
- Government > Military
- Cyberwarfare (1.00)
- Information Technology > Security & Privacy (1.00)
- Government > Military