ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies

Abstract

Semantic generation of network access control policies can help network administrators accurately implement policies to achieve desired security objectives. Current semantic generation research mainly focuses on semantic generation of single grammar and lacks work on automatically generating semantics for different grammatical strategies. Generating semantics for different grammars is a tedious, inefficient, and non-scalable task. Inspired by sequence labeling in the field of natural language processing, this article models automatic semantic generation as a sequence labeling task. We propose a semantic generation algorithm named ADLBiLSTM. The algorithm uses a self-attention mechanism and double-layer BiLSTM to extract the features of security policies from different aspects, so that the algorithm can flexibly adapt to policies of different complexity without frequent modification. Experimental results showed that the algorithm has good performance and can achieve high accuracy in semantic generation of access control list (ACL) and firewall data and can accurately understand and generate the semantics of network access control policies.