This document summarizes a research paper that proposes using data mining techniques to analyze network traffic and detect intrusions. The approach involves collecting network logs, extracting packet attributes, and applying classification algorithms like C4.5 decision trees to generate binary classifiers for different event classes. The classifiers are used to analyze the KDD network traffic dataset to detect patterns and evaluate combining multiple classifiers. Statistical analysis of false positives and negatives is also performed to help optimize intrusion detection and prevention systems. The proposed approach was tested on a local area network, with preprocessing of packet data and continuous monitoring of network protocols.