ML-FEED: Machine Learning Framework for Efficient Exploit Detection
Author:
Affiliation:
1. Princeton University,Electrical and Computer Engineering,NJ,USA
2. University of California,Electrical and Computer Engineering,Los Angeles,CA,USA
3. Cryptography Research Center Technology Innovation Institute,Abu Dhabi,UAE
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10063339/10063340/10063446.pdf?arnumber=10063446
Reference22 articles.
1. SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine learning
2. Vulnerability extrapolation: Assisted discovery of vulnerabilities using machine learning;yamaguchi;Proc Usenix Conf Offensive Technologies,2011
3. Fasttext.zip: Compressing text classification models;joulin,2016
4. GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security
5. SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
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