This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between “bad” connections, called intrusions or attacks, and “good” normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Souza, Vinicius M. A.; dos Reis, Denis M.; Maletzke, André G.; Batista, Gustavo E. A. P. A.: Challenges in benchmarking stream learning algorithms with real-world data (2020)
- Roshan, Setareh; Miche, Yoan; Akusok, Anton; Lendasse, Amaury: Adaptive and online network intrusion detection system using clustering and extreme learning machines (2018)
- Fercoq, Olivier; Richtárik, Peter: Optimization in high dimensions via accelerated, parallel, and proximal coordinate descent (2016)
- Kumar, G. Kishor; Viswanath, P.; Rao, A. Ananda: Ensemble of randomized soft decision trees for robust classification (2016)
- Costa, Kelton A. P.; Pereira, Luis A. M.; Nakamura, Rodrigo Y. M.; Pereira, Clayton R.; Papa, João P.; Xavier Falcão, Alexandre: A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks (2015)
- Fercoq, Olivier; Richtárik, Peter: Accelerated, parallel, and proximal coordinate descent (2015)
- Iglesias, Félix; Zseby, Tanja: Analysis of network traffic features for anomaly detection (2015) ioport
- Jiang, Feng; Sui, Yuefei; Zhou, Lin: A relative decision entropy-based feature selection approach (2015)
- Gretton, Arthur; Borgwardt, Karsten M.; Rasch, Malte J.; Schölkopf, Bernhard; Smola, Alexander: A kernel two-sample test (2012)