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Draper Mining and Understanding Software Enclaves (MUSE).
| 题名： | Draper Mining and Understanding Software Enclaves (MUSE). |
| 作者： | Ellingwood, P.; Enxing, H.; Hamilton, L.; Harer, J. |
| 关键词： | Cyber security, Data analytics, Deep learning, Machine learning, Software vulnerability detection, Software vulnerability repair |
| 摘要： | This report describes research carried out by The Charles Stark Draper Laboratory, Inc. (Draper) team in the Defense Advanced Research Projects Agency (DARPA) Mining and Understanding Software Enclaves (MUSE) program under contract FA8750-15-C-0242. Our focus on the MUSE program was to develop big-data analytics using machine learning for automatic vulnerability classification and repair. Key technical advancements that we contributed to the MUSE program included: (1) fast and scalable machine learning-based classifiers to detect patterns in known types of software vulnerabilities; (2) a generative adversarial network (GAN) to advance the state of the art in automated repair of common types of software vulnerabilities; and (3) a data ingestion pipeline to scrape, build, and analyze millions of functions from open-source software to generate training data for learning-based algorithms. |
| 报告类型： | 科技报告 |
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