Time Series Clustering for Anomaly Detection Using Competitive Neural Networks

Authors:
Guilherme A. Barreto;Leonardo Aguayo
Affiliations:
Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil CEP 60455-970;Nokia Institute of Technology (INdT), Brasília, Brazil CEP 70397-900
Venue:
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Year:
2009

Citing 7
Cited 6

Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system

Neural Networks
The temporal Kohonen map

Neural Networks
An Approach to Novelty Detection Applied to the Classification of Image Regions

IEEE Transactions on Knowledge and Data Engineering
Recursive self-organizing network models

Neural Networks - 2004 Special issue: New developments in self-organizing systems
SOM-based novelty detection using novel data

IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Min-max hyperellipsoidal clustering for anomaly detection in network security

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Condition monitoring of 3G cellular networks through competitive neural models

IEEE Transactions on Neural Networks

On building local models for inverse system identification with vector quantization algorithms

Neurocomputing
Detection of different authorship of text sequences through self-organizing maps and mutual information function

MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Robust neural network for novelty detection on data streams

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Improving the performance of neural networks with random forest in detecting network intrusions

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Review: A review of novelty detection

Signal Processing
Authorship attribution as a case of anomaly detection: A neural network model

International Journal of Hybrid Intelligent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we evaluate competitive learning algorithms in the task of identifying anomalous patterns in time series data. The methodology consists in computing decision thresholds from the distribution of quantization errors produced by normal training data. These thresholds are then used for classifying incoming data samples as normal/abnormal. For this purpose, we carry out performance comparisons among five competitive neural networks (SOM, Kangas' Model, TKM, RSOM and Fuzzy ART) on simulated and real-world time series data.