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

dc.contributor.authorPagano, Matthew W.
dc.date.accessioned2011-02-03T15:09:35Z
dc.date.available2011-02-03T15:09:35Z
dc.date.issued2011-02-03T15:09:35Z
dc.description.abstractIn 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.en_US
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/34449
dc.language.isoen_USen_US
dc.relation.ispartofseriesJohns Hopkins University Department of Computer Science, February 2011;Technical Report 11-01
dc.subjectLarge Datasetsen_US
dc.subjectOnline Learningen_US
dc.subjectPassive-Agressive Algorithmen_US
dc.subjectPerceptronen_US
dc.subjectDARPA Intrusion Detection Datasetsen_US
dc.subjectKDD Cup 1999 Dataseten_US
dc.subjectConfidence-Weighted Learningen_US
dc.subjectIntrusion Detectionen_US
dc.subjectMachine Learningen_US
dc.titleEvaluation of Diagonal Confidence-Weighted Learning on the KDD Cup 1999 Dataset for Network Intrusion Detection Systemsen_US
dc.typeTechnical Reporten_US
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