This paper proposes a new pattern matching architecture with multi-character processing for deep packet inspection. The proposed pattern matching architecture detects the start point of pattern matching from multi-character input using input text alignment. By eliminating duplicate hardware components using process element tree, hardware cost is greatly reduced in the proposed pattern matching architecture.

Fast string matching is essential for deep packet inspection (DPI). Traditional string matchers cannot keep up with the continuous increases in data rates due to their natural speed limits. We add a multi-byte processing prefilter to the traditional string matcher to detect target patterns on a multiple character basis. The proposed winnowing prefilter significantly reduces the number of identity blocks, thereby reducing the memory requirements.

Although IP address lookup schemes using ternary content addressable memory (TCAM) can perform high speed packet forwarding, TCAM is much more expensive than ordinary memory in implementation cost. As a low-cost solution, binary search algorithms such as a binary trie or a binary search tree have been widely studied. This paper proposes an efficient IP address lookup scheme using balanced binary search with minimal entries and optimal prefix vectors. In the previous scheme with prefix vectors, there were numerous pairs of nearly identical entries with duplicated prefix vectors. In our scheme, these overlapping entries are combined, thereby minimizing entries and eliminating the unnecessary prefix vectors. As a result, the small balanced binary search tree can be constructed and used for a software-based address lookup in small-sized routers. The performance evaluation results show that the proposed scheme offers faster lookup speeds along with reduced memory requirements.

This paper proposes a pattern partitioning algorithm that maps multiple target patterns onto homogeneous memory-based string matchers. The proposed algorithm adopts the greedy search based on lexicographical sorting. By mapping as many target patterns as possible onto each string matcher, the memory requirements are greatly reduced.