Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3322431.3326329 | Proceedings of the 24th ACM Symposium on Access Control Models and Technologies |
Keywords | Field | DocType |
access control, deep learning, word embeddings | Data mining,Computer science,Source code,Access control,Artificial intelligence,Enforcement,Deep learning,Word embedding,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6753-0 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Heaps | 1 | 1 | 2.09 |
| Xiaoyin Wang | 2 | 185 | 18.44 |
| Travis D. Breaux | 3 | 655 | 47.75 |
| Jianwei Niu | 4 | 275 | 26.61 |