Time Series Clustering for Anomaly Detection Using Competitive Neural Networks
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Fault Prediction in Aircraft Engines Using Self-Organizing Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Monitoring architechiture for UMTS networks
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Anomaly detection using self-organizing map and wavelets in wireless sensor networks
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Anomaly detection in wireless sensor networks using self-organizing map and wavelets
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.