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.
Highlights
Using machine learning techniques to solve computer security challenges is not a new idea
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 “A closer look at applications of deep learning in solving security-oriented program analysis challenges-A closer look at applications of deep learning in security-oriented fuzzing” section, we provide a review of eight computer security problems being solved by applications of Deep Learning, respectively
Summary
Using machine learning techniques to solve computer security challenges is not a new idea. Traditional CFI techniques typically leverage some knowledge, gained from either dynamic or static analysis of the target program, combined with some code instrumentation methods, to ensure the program runs on a correct track. Recent research has proposed to apply Deep Learning on detecting control flow violation Their result shows that, compared with traditional CFI implementation, the security coverage and scalability were enhanced in such a fashion (Yagemann et al 2019). Both the industry and the academic communities have provided approaches to detect malware with static and dynamic analyses Traditional methods such as behavior-based signatures, dynamic taint tracking, and static data flow analysis require experts to manually investigate unknown files. Those hand-crafted signatures are not sufficiently effective because attackers can rewrite and reorder the malware. Recent research has come up with ideas of applying Deep Learning in the process of fuzzing to solve these problems
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