Using Deep Learning to Solve Computer Security Challenges: A Survey

TL;DR

This survey reviews recent research on applying deep learning techniques to various computer security challenges, highlighting recent advances and applications in the field.

Contribution

It provides a comprehensive overview of how deep learning is being used to address multiple security problems, summarizing recent research efforts.

Findings

Deep learning enhances malware classification accuracy.

Deep learning techniques improve anomaly detection in systems.

Deep learning aids in defending against network attacks.

Abstract

Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges. In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security.