FEACAN: Front-end acceleration for content-aware network processing

FREME: A pattern partition based engine for fast and scalable regular expression matching in practice

“…It is known that there are a lot of DFA-based compression algorithms (Tuck et al, 2004;Kumar et al, 2006;Brodie et al, 2006;Becchi and Cadambi, 2007;Ficara et al, 2008;Meiners et al, 2010;Qi et al, 2011;Peng et al, 2011;van Lunteren and Guanella, 2012) proposed in the past decade. They focus on eliminating the redundant identical transitions inside or among the states, to reduce the memory overhead of each state in the DFA, and only work on the premise that DFA can be generated (i.e., nonexplosive).…”

Practical regular expression matching free of scalability and performance barriers

“…Generally, the related solutions can be categorized into four directions: regex grouping [7][8][9], alternative representation [10][11][12][13], hybrid construction [14][15][16][17], and transition compression [18][19][20][21][22][23][24][25][26][27][28][29].…”

“…Transition compression algorithms [18][19][20][21][22][23][24][25][26][27][28][29] focus on eliminating redundant transitions, which are identical inside or among the states, to reduce the memory consumption of DFA. D 2 FA proposed by Kumar et al [19] and A-DFA proposed by Becchi et al [21], which can achieve over 90% compression ratio, are the typical algorithms to remove the duplicated transitions among states by introducing the default transition.…”

SI-DFA: Sub-expression integrated Deterministic Finite Automata for Deep Packet Inspection

“…Consequently schemes trading DFA state space requirements with more than one state traversal per input byte have been presented [1], [2]. By introduction of a pipelined architecture, the problem of having a deterministic processing rate has also been solved by recent schemes, namely, FEACAN [3] and RCDFA [4].…”