This document discusses applying data mining techniques to network intrusion detection. It begins by introducing traditional intrusion detection methods and their limitations. It then establishes a data mining-based model for intrusion detection that is designed to address these limitations. The model collects network data, preprocesses it, applies data mining algorithms to extract patterns, and uses the patterns to detect intrusions. This allows detection of both known and unknown intrusion types while reducing false alarms and missed detections compared to traditional methods. Key aspects of the proposed system include data acquisition, preprocessing, pattern extraction using various data mining algorithms, and intrusion detection based on the extracted patterns.