Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
We present a dataset of adversarial malware samples derived from the public RawMal-TF collection of real-world malware binaries. Using a suite of adversarial malware generators, we construct two sets of adversarial PE files: 44,347 family-labelled samples and 33,596 type-labelled samples, achieving evasion rates of 98.35 % and 92.20 % against the EMBER classifier, respectively. Each adversarial binary is accompanied by detailed metadata, including EMBER scores and VirusTotal classifications. We further demonstrate the susceptibility of malware classification pipelines to data poisoning attacks through a series of training experiments. Injecting fully mislabelled adversarial samples repres...
| Authors | David Košťál, Martin Jureček |
|---|---|
| Primary category | cs.CR |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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