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Anomaly Intrusion Detection System Using Random Forests and K-Nearest Neighbor

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Executive Summary

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 Neighbors' 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.

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