Pattern matching for network intrusion/prevention detection requires extremely high throughput with frequent updates to support new attack patterns. Most of current hardware implementations have outstanding performance over software implementations. However, the requirement for dynamic update pattern set is still challenging for hardware researchers. This paper describes a novel FPGA-based pattern matching architecture using a recent hashing algorithm called Cuckoo Hashing. The proposed architecture features on-the-fly pattern updates without reconfiguration, more efficient hardware utilization, and higher performance. Through various algorithmic changes of Cuckoo Hashing, we can implement parallel pattern matching on SRAM-based FPGA. Our system can accommodate the latest Snort rule-set, an open source network intrusion detection/prevention system, and achieve the highest utilization in terms of SRAM per character and logic cells per character at 17 bits/character and 0.043 logic cells/character, respectively on major Xilinx Virtex architectures. Compared to others, ours is much more efficient than any other Xilinx FPGA architectures.

This paper presents a reconfigurable processor array for the similarity search of up to sixteen DNA query sequences against a large database. Based on the recently proposed space-time mapping methodology, the processor array is implemented using a Xilinx Virtex-4 XC4VLX60 FPGA on the AVNet ADS-XLX-V4-LX-EVL60 board. The proposed system can sustain a high computation throughput rate with lower data transfer speed from the host database than others up to sixteen folds via a USB 2.0 interface.