Malware Detection based on API Calls: A Reproducibility Study
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908761677889536 |
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| author | Merilehto, Juhani |
| author_facet | Merilehto, Juhani |
| contents | This study independently reproduces the malware detection methodology presented by Felli cious et al. [7], which employs order-invariant API call frequency analysis using Random Forest classification. We utilized the original public dataset (250,533 training samples, 83,511 test samples) and replicated four model variants: Unigram, Bigram, Trigram, and Combined n gram approaches. Our reproduction successfully validated all key findings, achieving F1-scores that exceeded the original results by 0.99% to 2.57% across all models at the optimal API call length of 2,500. The Unigram model achieved F1=0.8717 (original: 0.8631), confirming its ef fectiveness as a lightweight malware detector. Across three independent experimental runs with different random seeds, we observed remarkably consistent results with standard deviations be low 0.5%, demonstrating high reproducibility. This study validates the robustness and scientific rigor of the original methodology while confirming the practical viability of frequency-based API call analysis for malware detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08725 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Malware Detection based on API Calls: A Reproducibility Study Merilehto, Juhani Cryptography and Security This study independently reproduces the malware detection methodology presented by Felli cious et al. [7], which employs order-invariant API call frequency analysis using Random Forest classification. We utilized the original public dataset (250,533 training samples, 83,511 test samples) and replicated four model variants: Unigram, Bigram, Trigram, and Combined n gram approaches. Our reproduction successfully validated all key findings, achieving F1-scores that exceeded the original results by 0.99% to 2.57% across all models at the optimal API call length of 2,500. The Unigram model achieved F1=0.8717 (original: 0.8631), confirming its ef fectiveness as a lightweight malware detector. Across three independent experimental runs with different random seeds, we observed remarkably consistent results with standard deviations be low 0.5%, demonstrating high reproducibility. This study validates the robustness and scientific rigor of the original methodology while confirming the practical viability of frequency-based API call analysis for malware detection. |
| title | Malware Detection based on API Calls: A Reproducibility Study |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2601.08725 |