The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Improving intrusion detection performance using keyword selection and neural networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Classification of Business Travelers Using SVMs Combined with Kernel Principal Component Analysis
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Traffic flooding attack detection with SNMP MIB using SVM
Computer Communications
Intrusion Detection Method Using Neural Networks Based on the Reduction of Characteristics
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A novel packet header visualization methodology for network anomaly detection
CNIS '07 Proceedings of the Fourth IASTED International Conference on Communication, Network and Information Security
Feature selection using rough-DPSO in anomaly intrusion detection
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
PCA for improving the performance of XCSR in classification of high-dimensional problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong
Expert Systems with Applications: An International Journal
A comparative study of dimensionality reduction techniques to enhance trace clustering performances
Expert Systems with Applications: An International Journal
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Network intrusion detection is an important technique in computer security. However, the performance of existing intrusion detection systems (IDSs) is unsatisfactory since new attacks are constantly developed and the speed of network traffic volumes increases fast. To improve the performance of IDSs both in accuracy and speed, this paper proposes a novel adaptive intrusion detection method based on principal component analysis (PCA) and support vector machines (SVMs). By making use of PCA, the dimension of network data patterns is reduced significantly. The multi-class SVMs are employed to construct classification models based on training data processed by PCA. Due to the generalization ability of SVMs, the proposed method has good classification performance without tedious parameter tuning. Dimension reduction using PCA may improve accuracy further. The method is also superior to SVMs without PCA in fast training and detection speed. Experimental results on KDD-Cup99 intrusion detection data illustrate the effectiveness of the proposed method.