A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet Inspection

Abstract

Construction and deployment of finite state automata from the regular expressions might results in huge overhead and results in the state explosion problem which is in need of large memory space, high bandwidth and additional computational time. To overcome this problem, a new framework is proposed, and several intelligent optimization algorithms are reviewed and compared in this framework. The proposed approach is called intelligent optimization grouping algorithms (IOGA), which intends to group regular expression intelligently. IOGAs are used to allocate the regular expression sets into various groups and to build independent deterministic finite automata (DFA) for each group. Grouping the regular expression efficiently solves the state explosion problem by achieving large-scale best tradeoff among memory utilization and computational time. This study reviews and compares the various alternatives of IOGA including genetic algorithm, ant colony optimization, particle swarm optimization, bacterial foraging optimization, artificial bee colony algorithm, biogeography based optimization, cuckoo search, firefly algorithm, bat algorithm and flower pollination algorithm for solving the problem of DFA state explosion and also for improving the overall efficiency of deep packet inspection (DPI). The discussions state that by effectively using these grouping algorithms along with DFA based DPI, the number of states can be reduced, providing a balance between the memory consumption, time complexity, throughput, inspection speed, convergence speed and grouping time.