Automated Malware Relationship Mining

Since last year, Holmes-Processing has acquired a large dataset of labeled malware samples, which can be used for deep learning based malware relationship mining. This labeled dataset of over 50k samples should be a big help to do malware relationship detection. Besides, as a result of the previous GSoC’17, we also have an efficient data model for the malware relationships.

Therefore, the goals of this project are to

1. implement a decent learning model to predict labels of each malware sample
2. discover relationships between different malware samples
3. visualize relationships in frontend
4. and build an analytic pipeline to integrate the implemented services.