Assemblage: Automatic Binary Dataset Construction for Machine Learning
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913570030092288 |
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| author | Liu, Chang Saul, Rebecca Sun, Yihao Raff, Edward Fuchs, Maya Pantano, Townsend Southard Holt, James Micinski, Kristopher |
| author_facet | Liu, Chang Saul, Rebecca Sun, Yihao Raff, Edward Fuchs, Maya Pantano, Townsend Southard Holt, James Micinski, Kristopher |
| contents | Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corpora of malicious binaries, obtaining high-quality corpora of benign binaries for modern systems has proven challenging (e.g., due to licensing issues). Consequently, machine learning based pipelines for binary analysis utilize either costly commercial corpora (e.g., VirusTotal) or open-source binaries (e.g., coreutils) available in limited quantities. To address these issues, we present Assemblage: an extensible cloud-based distributed system that crawls, configures, and builds Windows PE binaries to obtain high-quality binary corpuses suitable for training state-of-the-art models in binary analysis. We have run Assemblage on AWS over the past year, producing 890k Windows PE and 428k Linux ELF binaries across 29 configurations. Assemblage is designed to be both reproducible and extensible, enabling users to publish "recipes" for their datasets, and facilitating the extraction of a wide array of features. We evaluated Assemblage by using its data to train modern learning-based pipelines for compiler provenance and binary function similarity. Our results illustrate the practical need for robust corpora of high-quality Windows PE binaries in training modern learning-based binary analyses. Assemblage code is open sourced under the MIT license, and the dataset can be downloaded from https://assemblage-dataset.net |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03991 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assemblage: Automatic Binary Dataset Construction for Machine Learning Liu, Chang Saul, Rebecca Sun, Yihao Raff, Edward Fuchs, Maya Pantano, Townsend Southard Holt, James Micinski, Kristopher Cryptography and Security Machine Learning Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corpora of malicious binaries, obtaining high-quality corpora of benign binaries for modern systems has proven challenging (e.g., due to licensing issues). Consequently, machine learning based pipelines for binary analysis utilize either costly commercial corpora (e.g., VirusTotal) or open-source binaries (e.g., coreutils) available in limited quantities. To address these issues, we present Assemblage: an extensible cloud-based distributed system that crawls, configures, and builds Windows PE binaries to obtain high-quality binary corpuses suitable for training state-of-the-art models in binary analysis. We have run Assemblage on AWS over the past year, producing 890k Windows PE and 428k Linux ELF binaries across 29 configurations. Assemblage is designed to be both reproducible and extensible, enabling users to publish "recipes" for their datasets, and facilitating the extraction of a wide array of features. We evaluated Assemblage by using its data to train modern learning-based pipelines for compiler provenance and binary function similarity. Our results illustrate the practical need for robust corpora of high-quality Windows PE binaries in training modern learning-based binary analyses. Assemblage code is open sourced under the MIT license, and the dataset can be downloaded from https://assemblage-dataset.net |
| title | Assemblage: Automatic Binary Dataset Construction for Machine Learning |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2405.03991 |