A data mining project to detect network intrusion and identify intrusion types

This project aims to detect unusual network activities in a networking system. The original dataset comes from the XYZ Bank’s historical log files. To distinguish intrusions from benign sessions, we applied four classification methods on our training dataset, which includes Naive Bayes, Random Forest, Boosting, and K-Nearest Neighbours. Then we performed cross validation to evaluate the predicting power of each method. To identify different types of intrusions, we conducted K-Means clustering and grouped the intrusions into 3 types based on various combinations of attributes.

In sum, our system provides a holistic approach to detecting intrusions and identifying the types. It is helpful in protecting cyber security and users’ privacy.