Using Deep Learning to Solve Computer Security Challenges: A Survey

December 12, 2019 ยท The Cartographer ยท ๐Ÿ› Cybersecurity

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: Using Deep Learning to Solve Computer Security Challenges: A Survey"

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Authors Yoon-Ho Choi, Peng Liu, Zitong Shang, Haizhou Wang, Zhilong Wang, Lan Zhang, Junwei Zhou, Qingtian Zou arXiv ID 1912.05721 Category cs.CR: Cryptography & Security Citations 35 Venue Cybersecurity Last Checked 2 days ago
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.
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