Evaluation of Diagonal Confidence-Weighted Learning on the KDD Cup 1999 Dataset for Network Intrusion Detection Systems

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2011-02-03T15:09:35Z
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Abstract
In this study, I evaluate the performance of diagonal Confidence-Weighted (CW) online linear classification on the KDD Cup 1999 dataset for network intrusion detection systems (NIDS). This is a compatible relationship due to the large number of instances in NIDS datasets, as well as the constantly changing feature distributions. CW learning achieves approximately 92% accuracy on the KDD dataset when optimized, which is higher than both Perceptron and the Passive-Aggressive algorithm. CW learning also achieves faster convergence rates than both of these algorithms. Moreover, the accuracy of CW learning on the KDD dataset is comparable to several batch-learning algorithms. This challenges the assumption that batch learning should always be used when feasible. Due to shortcomings of the KDD dataset, a full generalization of CW learning to additional NIDS environments cannot yet be made. Nonetheless, this study shows that there is great promise to applying CW learning to future NIDS research.
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Large Datasets, Online Learning, Passive-Agressive Algorithm, Perceptron, DARPA Intrusion Detection Datasets, KDD Cup 1999 Dataset, Confidence-Weighted Learning, Intrusion Detection, Machine Learning
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