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首页> 外文会议>International Conference on Malicious and Unwanted Software >Static Malware Detection Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus
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Static Malware Detection Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus

机译：静态恶意软件检测和替代：量化机器学习和当前反病毒的鲁棒性

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摘要

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.
机译：随着基于机器学习（ML）的恶意软件检测系统日益普及，与当今广泛使用的更传统的防病毒（AV）系统相比，有必要量化收益。建立商定的测试集以纯粹的分类性能对恶意软件检测系统进行基准测试是不切实际的。相反，我们通过创建一种新的测试方法来解决该问题，该方法将在进行对抗性修改时评估一组已知的良性和恶意文件的性能变化。然后，性能的变化与规避技术相结合，从而量化了该方法相对于该方法的鲁棒性。通过这些实验，我们能够以定量的方式证明基于ML的系统在检测试图通过修改进行回避的恶意软件方面比AV产品更健壮，但是面对明显的新型攻击时适应速度较慢。

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