A hybrid network intrusion detection framework based on random forests and weighted k-means

(in lingua inglese)

This paper discusses the data-mining-based network intrusion detection systems. Two data-mining techniques are used in misuse, anomaly, and hybrid detection. First, the random forests algorithm is used as a data mining classification algorithm into a misuse detection method to build intrusion patterns from a balanced training dataset, and to classify the captured network connections to the main types of intrusions due to the built patterns. Our method is implemented in C#.NET by using the random forest original implementation [17] and tested through the KDD’99 datasets.