Compass Security Blog

Offensive Defense

Evading Static Machine Learning Malware Detection Models – Part 2: The Gray-Box Approach

This part will discuss a grey-box approach in defeating malware detection. It will discuss the relevant features used and how they are fed into a malware detection model to classify an input file. After a short theory part, we try to find out which features are especially important for malware analysis and how to modify them. Finally, we will change some of the features of our ransomware to evade our model. But before all this, it is advisable to get familiar with the file format used by our malware.

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Evading Static Machine Learning Malware Detection Models – Part 1: The Black-Box Approach

Modern anti-malware products such as Windows Defender increasingly rely on the use of machine learning algorithms to detect and classify harmful malware. In this two-part series, we are going to investigate the robustness of a static machine learning malware detection model trained with the EMBER dataset. For this purpose we will working with the Jigsaw ransomware.

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