Online Double Random Forests Intrusion Detection Based on Non-extensive Entropy Features Extraction
Yao Don · 2013
This paper proposed an intrusion detection method that can be used in high speed network backbone.Based on non-extensive entropy with different parameters,the original distribution of the values of attributes was decomposed to high dimensional features.Using these detailed features,the detection model based on random forest was constructed.For the purpose of increasing detection accuracy and recall further,the second random forest detection model was constructed with the attack instances only.The experimental results suggest that proposed intrusion detection method can achieve competitive detection precision with a high recall.