Machine learning and data mining algorithms are increasingly being used in the intrusion detection systems (IDS), but their performances are laggard to some extent especially applied in network based intrusion detection: the larger load of network traffic monitoring requires more efficient algorithm in practice. In this paper, we propose and design an anomaly intrusion detection (AID) system based on the vector quantization (VQ) which is widely used for data compression and high-dimension multimedia data index. The design procedure optimizes the performance of intrusion detection by jointly accounting for accurate usage profile modeling by the VQ codebook and fast similarity measures between feature vectors to reduce the computational cost. The former is just the key of getting high detection rate and the later is the footstone of guaranteeing efficiency and real-time style of intrusion detection. Experiment comparisons to other related researches show that the performance of intrusion detection is improved greatly.