Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Intrusion detection
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
AI Communications - Special issue on Artificial intelligence advances in China
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Feature selection with adjustable criteria
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Feature selection based on relative attribute dependency: an experimental study
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
An application of covering approximation spaces on network security
Computers & Mathematics with Applications
Network intrusion detection based on multi-class support vector machine
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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Design and implementation of intrusion detection systems remain an important research issue in order to maintain proper network security. Support Vector Machines (SVM) as a classical pattern recognition tool have been widely used for intrusion detection. However, conventional SVM methods do not concern different characteristics of features in building an intrusion detection system. We propose an enhanced SVM model with a weighted kernel function based on features of the training data for intrusion detection. Rough set theory is adopted to perform a feature ranking and selection task of the new model. We evaluate the new model with the KDD dataset and the UNM dataset. It is suggested that the proposed model outperformed the conventional SVM in precision, computation time, and false negative rate