Using deep learning to solve computer security challenges: a survey
Using deep learning to solve computer security challenges: a survey
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DOI：
10.1186/s42400-020-00055-5
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发表时间：
2020-08-10
期刊：
影响因子：
3.1
通讯作者：
Zou, Qingtian
中科院分区：
文献类型：
--
作者：
Choi, Yoon-Ho;Liu, Peng;Zou, Qingtian
尽管使用机器学习技术来解决计算机安全挑战并不是一个新想法，但迅速发展的深度学习技术最近引发了计算机安全社区的大量兴趣。本文旨在对最新的研究作品进行专门评论，以使用深度学习技术来解决计算机安全挑战。特别是，该评论涵盖了通过深度学习的应用程序解决的八个计算机安全问题：面向安全的程序分析，捍卫面向返回的编程（ROP）攻击，实现控制流程完整性（CFI），捍卫网络攻击，恶意软件分类，基于系统事件的异常检测，内存取证和软件安全性的模糊。
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.