Toward Detection of Access Control Models from Source Code via Word Embedding
| Autor: | Xiaoyin Wang, Jianwei Niu, John Heaps, Travis D. Breaux |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Word embedding
Source code business.industry Computer science Deep learning media_common.quotation_subject 020207 software engineering Access control 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Work (electrical) 0202 electrical engineering electronic engineering information engineering Code (cryptography) Artificial intelligence business Enforcement computer Word (computer architecture) 0105 earth and related environmental sciences media_common |
| Zdroj: | SACMAT |
| DOI: | 10.1145/3322431.3326329 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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