C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
Ensemble of One-Class Classifiers for Network Intrusion Detection System
IAS '08 Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security
McPAD: A multiple classifier system for accurate payload-based anomaly detection
Computer Networks: The International Journal of Computer and Telecommunications Networking
Network intrusion detection by combining one-class classifiers
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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The dramatic proliferation of sophisticated cyber attacks, in conjunction with the ever growing use of Internet-based services and applications, is nowadays becoming a great concern in any organization. Among many efficient security solutions proposed in the literature to deal with this evolving threat, ensemble approaches, a particular family of data mining, have proven very successful in designing high performance intrusion detection systems (IDSs) resting on the mutual combination of multiple classifiers. However, the strength of ensemble systems depends heavily on the methods to generate and combine individual classifiers. In this thread, we propose a novel design method to generate a robust ensemble-based IDS. In our approach, individual classifiers are built using both the input feature space and additional features exploited from k-means clustering. In addition, the ensemble combination is calculated based on the classification ability of classifiers on different local data regions defined in form of k-means clustering. Experimental results prove that our solution is superior to several well-known methods.