As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today. It is not practical to build an agreed upon test set to benchmark malware detection systems on pure classification performance. Instead we tackle the problem by creating a new testing methodology, where we evaluate the change in performance on a set of known benign & malicious files as adversarial modifications are performed. The change in performance combined with the evasion techniques then quantifies a system's robustness against that approach. Through these experiments we are able to show in a quantifiable way how purely ML based systems can be more robust than AV products at detecting malware that attempts evasion through modification, but may be slower to adapt in the face of significantly novel attacks.

通过创建新的测试方法，我们评估了针对一组已知的Benign & Malicious文件执行敌对修改并评估性能变化的变化以及逃避技术，从而依靠实验证明了仅基于ML的系统可以比AV产品更有效地检测到试图通过修改来逃避的恶意软件，但在面临显着的新型攻击时可能反应较慢。

静态恶意软件检测与隐蔽: 量化机器学习和当前反病毒软件的稳健性