Compressing Regular Expressions' DFA Table by Matrix Decomposition

Yanbing Liu, Li Guo, Ping Liu, Jianlong Tan

Abstract: Recently regular expression matching has become a research focus as a result of the urgent demand for Deep Packet Inspection (DPI) in many network security systems. Deterministic Finite Automaton (DFA), which recognizes a set of regular expressions, is usually adopted to cater to the need for real-time processing of network traffic. However, the huge memory usage of DFA prevents it from being applied even on a medium-sized pattern set. In this article, we propose a matrix decomposition method for DFA table compression. The basic idea of the method is to decompose a DFA table into the sum of a row vector, a column vector and a sparse matrix, all of which cost very little space. Experiments on typical rule sets show that the proposed method significantly reduces the memory usage and still runs at fast searching speed.