MalDICT: Benchmark Datasets on Malware Behaviors, Platforms, Exploitation, and Packers. (arXiv:2310.11706v1 [cs.CR])

Existing research on malware classification focuses almost exclusively on two
tasks: distinguishing between malicious and benign files and classifying
malware by family. However, malware can be categorized according to many other
types of attributes, and the ability to identify these attributes in
newly-emerging malware using machine learning could provide significant value
to analysts. In particular, we have identified four tasks which are
under-represented in prior work: classification by behaviors that malware
exhibit, platforms that malware run on, vulnerabilities that malware …

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