Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context

By Maheshkumar Sabhnani, Gürsel Serpen. International Conference on Machine Learning; Models, Technologies and Applications, 2003

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A Supervised Intrusion Detection System for Smart Home IoT Devices (2019, background): “, [51], [52]), nine classifiers were selected based on their ability to support multiclass classification, highdimensional feature space, and the time it takes for the classification model to classify unseen data.”
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, background): “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).”
CNN-Based Network Intrusion Detection against Denial-of-Service Attacks (2020, background): “[23] evaluate the performance of a comprehensive set of pattern recognition and machine learning algorithms in four attack categories of KDD.”
Unsupervised Learning Trojan (2014, background): “Among the applications of machine learning are financial applications (Krollner et al., 2010; McNelis, 2005), medical applications (Kononenko, 2001), and intrusion detection (Tsai et al., 2009; Sabhnani & Serpen, 2003a), among many others.”