Towards a taxonomy of intrusion-detection systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on computer network security
Is negative selection appropriate for anomaly detection?
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning States and Rules for Detecting Anomalies in Time Series
Applied Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Applicability issues of the real-valued negative selection algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A wireless intrusion detection method based on neural network
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
Probabilistic techniques for intrusion detection based on computer audit data
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The growing number of unauthorized activities and various trends of networking technologies in telecommunication network have added heavy burdens to telecommunication performance management (PM) system. One-class-support vector machine (OCSVM) is introduced in this paper, to automatically detect network anomalies. Real telecommunication performance data are employed in this paper to investigate the feasibility of OCSVM for anomaly detection. Experiments with small and large data sets demonstrate that OCSVM can not only detect the anomalies correctly, but also fast in a short time. The promising performances show that OCSVM is efficiently enough to meet with the anomaly detection task in telecommunication network.