Toward Detection of Access Control Models from Source Code via Word Embedding

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.