Random Forests Algorithm with Feature Grouping in Network Intrusion Detection
Sheng Li · Computers & Security · 2009
Intrusion detection is one of the important application areas of data mining. At present, there are many approaches of intrusion detection based on data mining. Although random forests method has shown better performance than some other methods, but it still has some problems. After analyzing the network intrusion data set we get the relationship between the different input features and the result of classification, so we propose a new random forests algorithm based on feature grouping, and then we applied it in KDD’99 data set. The test result of our new algorithm show that this method is much better than before in accuracy and speed.