Data compression techniques significantly improve storage efficiency, bandwidth utilization, and energy efficiency, yet they introduce challenges for the rapid browsing and retrieval of valuable information within compressed data. Existing approaches achieve high-speed, lossless matching by exploiting the context-free property of automata. However, they are constrained by the recursive reference structures in compressed data, which necessitate state copying to ensure matching safety.

For comprehensive analyzing or efficient searching from massive data, string matching is widely used as a core technique of the network traffic detection applications and text editors. However, the increasing compressed text challenges string matching to achieve high-speed processing. In this paper, we propose KCM, a fast Knuth-Morris-Pratt based string matching method over LZ77 compressed text. It leverages the gathered heuristic information during scanning to skip the characters that should have been scanned. In our evaluation with real traffic, KCM skips more than 90% compression text, which nearly approaches the theoretical upper bound. It can achieve 1.61 Gbps throughput and boost 1.87 times than the classic string matching.