一句话：提出利用已知处理器勘误与机器学习推断安全关键不变量，以支撑处理器动态验证的方法。
处理器动态验证是防止漏洞利用的有效手段，但其效果依赖于一组有意义的 security properties。针对如何获得这类属性，本文提出一套识别安全关键属性的方法：首先利用已知的处理器 errata 建立初始的 security-critical invariants；随后借助机器学习推断额外的、并不绑定于任何已知漏洞、但对安全至关重要的 invariants。摘要未给出具体实验数据或量化结果，因此论文的主要贡献是提出这一属性识别流程，为处理器动态验证提供超越已知漏洞的 security property 集。
We present a methodology for identifying security critical properties for use in the dynamic verification of a processor. Such verification has been shown to be an effective way to prevent exploits of vulnerabilities in the processor, given a meaningful set of security properties. We use known processor errata to establish an initial set of security-critical invariants of the processor. We then use machine learning to infer an additional set of invariants that are not tied to any particular, known vulnerability, yet are critical to security.
@inproceedings{ZhangSGCS17,
title = {Identifying Security Critical Properties for the Dynamic Verification of a Processor},
author = {Rui Zhang and Natalie Stanley and Christopher Griggs and Andrew Chi and Cynthia Sturton},
booktitle = {ASPLOS},
year = {2017},
doi = {10.1145/3037697.3037734}
}