Using Deep Learning to Solve Computer Security Challenges: A Survey
arXiv:1912.05721
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.
arXiv comment43 pages with 7 figures and two tables
References in corpus (4)
- DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
- Not all bytes are equal: Neural byte sieve for fuzzing
- DeepCheck: A Non-intrusive Control-flow Integrity Checking based on Deep Learning
- GPT Conjecture: Understanding the Trade-offs between Granularity, Performance and Timeliness in Control-Flow Integrity