Scalable multi-pipeline architecture for high performance multi-pattern string matching

Multi-pattern string matching remains a major performance bottleneck in network intrusion detection and anti-virus systems for high-speed deep packet inspection (DPI). Although Aho-Corasick deterministic finite automaton (AC-DFA) based solutions produce deterministic throughput and are widely used in today's DPI systems such as Snort [1] and ClamAV [2], the high memory requirement of AC-DFA (due to the large number of state transitions in AC-DFA) inhibits efficient hardware implementation to achieve high performance. Some recent work [3], [4] has shown that the AC-DFA can be reduced to a character trie that contains only the forward transitions by incorporating pipelined processing. But they have limitations in either handling long patterns or extensions to support multi-character input per clock cycle to achieve high throughput. This paper generalizes the problem and proves formally that a linear pipeline with H stages can remove all cross transitions to the top H levels of a AC-DFA. A novel and scalable pipeline architecture for memory-efficient multi-pattern string matching is then presented. The architecture can be easily extended to support multi-character input per clock cycle by mapping a compressed AC-DFA [5] onto multiple pipelines. Simulation using Snort and ClamAV pattern sets shows that a 8-stage pipeline can remove more than 99% of the transitions in the original AC-DFA. The implementation on a state-of-the-art field programmable gate array (FPGA) shows that our architecture can store on a single FPGA device the full set of string patterns from the latest Snort rule set. Our FPGA implementation sustains 10+ Gbps throughput, while consuming a small amount of on-chip logic resources. Also desirable scalability is achieved: the increase in resource requirement of our solution is sub-linear with the throughput improvement.