Anomaly Intrusion Detection System using Random Forests and k-Nearest Neighbor
Journal Title: International Journal of P2P Network Trends and Technology(IJPTT) - Year 2013, Vol 3, Issue 1
Abstract
This paper proposed a new approach to design the anomaly intrusion detection system using not only misuse but also anomaly intrusion detection for both training and detection of normal or attacks respectively. The utilized method is the combination of Machine Learning and pattern recognition method for Anomaly Intrusion Detection System(AIDS). The Machine Learning Algorithm, Random Forest, use as a feature selection method and the pattern recognition algorithm, k Nearest Neighbours for detection and classification of the known and unknown attack classes. The experimental results are obtained by using through intrusion dataset: the KDD Cup 1999 dataset.
Authors and Affiliations
Phyu Thi Htun1 , Kyaw Thet Khaing
Keywords
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