Lurng-Kuo Liu is a Solutions Architect and RSM at IBM T.J. Watson Research Center. He is also an Adjunct Professor at Columbia University. He is currently leading several emerging solutions development projects as part of IBM’s strategy directions for Cell Broadband Engine (Cell/B.E.) processor. Prior to his current position, he was a Program Manager for the Blue Gene (BG/L) System at IBM’s Explorer Server Systems department, where he has lead to the success of BG/L and ranked as No. 1 in the top 500 supercomputer list. He has worked on a broad range of projects such as video codec processors, media signal processor, broadband e-commerce, interactive TV, Set-Top Box, MP3 audio, video compression (MPEG-2, MPEG-4, H.263, etc.), immersion computer game systems, vision-enhanced human computer user interface (HCI) system, financial engineering, and high performance computing (HPC) system. His research interests include digital signal processing, multimedia, computer vision, broadband e-business, multi-core computing, bioinformatics, financial modeling, and HPC. Dr. Liu received his Ph.D. in Electrical Engineering at University of Maryland at College Park in 1993.

Dr. Liu will discuss LARGE SCALE PATTERN MATCHING FOR INTRUSION DETECTION. With the dramatic growth in the Internet applications, the constant increase in wire speed, and the increasing size of intrusion signature database, high performance system that is capable of processing deep packet inspection at high speed against a large scale dictionary has become an essential building block for realizing new generations of network intrusion detection systems (NIDS). Pattern matching is known to be one of the critical parts of almost every modern NIDS. A highly efficient and scalable pattern matching engine is crucial to NIDSs that require simultaneous matching of hundreds of thousands of patterns at wire speed. In this talk, we will discuss a large scale pattern matching scheme based on BART-based FSM (BFSM) that exploits the multicore computing power of the Cell Broadband Engine (Cell/B.E.) processor. The BFSM-based pattern matching scheme provides high storage efficiency with deterministic pattern matching performance. It also provides dynamic updates feature that allows patterns to be added and removed dynamically from the engine’s dictionary. This talk outlines approaches for exploiting multicore computing power to realize a scalable parallel large scale pattern matching engine for intrusion detection.