Results of the KDD'99 classifier learning

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A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, methodology): “The winner of the KDD’99 contest achieved an accuracy rate of 0.9271, an error rate of 0.0729, and an average cost of 0.2331 on the test dataset (Elkan, 2000; Sabhnani & Serpen, 2003).”
NETWORK INTRUSION DETECTION USING NAÏVE BAYES (2007, methodology): “The KDD’99 dataset is a subset of DARPA dataset prepared by Sal Stolfo and Wenke Lee [18].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “The KDD dataset is used for evaluation and shows that IMM outperforms three related works [131, 139, 363] in terms of detection rate and FP.”
Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context (2003, neutral): “An entry at row i and column j, C(i,j), represents the non-negative cost of misclassifying a pattern belonging to class i into class j. Cost matrix values employed for the KDD dataset are definedelsewherein[9] .”
The use of computational intelligence in intrusion detection systems: A review (2010, background): “The second-placed entry consisted of a decision forest with 755 trees [85].”
Random-Forests-Based Network Intrusion Detection Systems (2008, methodology): “We use the cost matrix as Table II published in the KDD’99 [14] to measure the damage of misclassification.”