Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
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arXiv
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| Natura: | Preprint |
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2026
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| _version_ | 1866917531752595456 |
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| author | Košťál, David Jureček, Martin |
| author_facet | Košťál, David Jureček, Martin |
| contents | 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 representing only 0.5 % of the training data in the family-labelled dataset increases the evasion rate against the re-trained classifier from 26.1 % to 92.8 %. The dataset is publicly released to facilitate future research on adversarial malware, poisoning attacks, and the robustness of machine-learning-based malware detection systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_25937 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation Košťál, David Jureček, Martin Cryptography and Security Machine Learning 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 representing only 0.5 % of the training data in the family-labelled dataset increases the evasion rate against the re-trained classifier from 26.1 % to 92.8 %. The dataset is publicly released to facilitate future research on adversarial malware, poisoning attacks, and the robustness of machine-learning-based malware detection systems. |
| title | Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2605.25937 |