ML-FEED: Machine Learning Framework for Efficient Exploit Detection (Extended version). (arXiv:2301.04314v1 [cs.CR])

Machine learning (ML)-based methods have recently become attractive for
detecting security vulnerability exploits. Unfortunately, state-of-the-art ML
models like long short-term memories (LSTMs) and transformers incur significant
computation overheads. This overhead makes it infeasible to deploy them in
real-time environments. We propose a novel ML-based exploit detection model,
ML-FEED, that enables highly efficient inference without sacrificing
performance. We develop a novel automated technique to extract vulnerability
patterns from the Common Weakness Enumeration (CWE) and Common Vulnerabilities
and Exposures (CVE) databases. This …

art automated computation detection environments exploit exploits extract framework machine machine learning ml models novel patterns performance security security vulnerability state transformers version vulnerability