Efficient Access Control in Wireless Network

Abstract

To use role-based access control (RBAC) in wireless network is difficult than that in wired network. RBAC needs to search relative tables to get the user's permissions. We present an access control judgment algorithm which bases on artificial neural network (ANN). The algorithm reduces the data transmission using bit string to express roles and permissions. The algorithm employs set theory to represent roles and their inheritance hierarchy, as well as conflicted permissions. It uses selected roles as input vectors and the matching permissions which contain no conflict as the output vectors to train the ANN. Then it uses the trained ANN to compute directly users' permissions when the system is under running condition, instead of searching tables. That improves the efficiency of access control. The algorithm is simple and efficient, which makes it easy to be realized in wireless networks