Toward Detection of Access Control Models from Source Code via Word Embedding
Toward Detection of Access Control Models from Source Code via Word Embedding
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DOI：
10.1145/3322431.3326329
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
John Heaps;Xiaoyin Wang;T. Breaux;Jianwei Niu
中科院分区：
文献类型：
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作者：
John Heaps;Xiaoyin Wang;T. Breaux;Jianwei Niu
近年来机器学习技术的进步导致了源代码上的深度学习应用。尽管有关该主题的研究很少，但已完成的工作显示出巨大的潜力。我们相信可以利用深度学习来获得对自动访问控制策略验证的新见解。在本文中，我们描述了将学习技术应用于访问控制的第一步，其中包括开发词嵌入来引导学习任务。我们还讨论了识别访问控制执行代码和检查访问控制策略违规的未来工作，这可以通过字嵌入来实现。
Advancement in machine learning techniques in recent years has led to deep learning applications on source code. While there is little research available on the subject, the work that has been done shows great potential. We believe deep learning can be leveraged to obtain new insight into automated access control policy verification. In this paper, we describe our first step in applying learning techniques to access control, which consists of developing word embeddings to bootstrap learning tasks. We also discuss the future work on identifying access control enforcement code and checking access control policy violations, which can be enabled by word embeddings.