Machine Learning
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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Neural Computation
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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
McPAD: A multiple classifier system for accurate payload-based anomaly detection
Computer Networks: The International Journal of Computer and Telecommunications Networking
ACM Computing Surveys (CSUR)
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ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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ACM Transactions on Intelligent Systems and Technology (TIST)
Ensemble learning with imbalanced data
Ensemble learning with imbalanced data
Automatic network intrusion detection: Current techniques and open issues
Computers and Electrical Engineering
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We show that using random forests and distance-based outlier partitioning with ensemble voting methods for supervised learning of anomaly detection provide similar accuracy results when compared to the same methods without partitioning. Further, distance-based outlier and one-class support vector machine partitioning and ensemble methods for semi-supervised learning of anomaly detection also compare favorably to the corresponding nonensemble methods. Partitioning and ensemble methods would be required for very large datasets that need distributed computing approaches. ROC curves often show significant improvement from increased true positives in the low false positive range for ensemble methods used on several datasets.