Abstract
The success of intelligent diagnosis systems normally depends on the knowledge about the failures present on monitored systems. This knowledge can be modelled in several ways, such as by means of rules or probabilistic models. These models are validated by checking the system output fit to the input in a supervised way. However, when there is no such knowledge or when it is hard to obtain a model of it, it is alternatively possible to use an unsupervised method to detect anomalies and failures. Different unsupervised methods (HCL, K-Means, SOM) have been used in present work to identify abnormal behaviours on the system being monitored. This approach has been tested into a real-world monitored system related to the railway domain, and the results show how it is possible to successfully identify new abnormal system behaviours beyond those previously modelled well-known problems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Gonzalez, A.J., Dankell, D.D.: Engineering of knowledge-based systems. Prentice-Hall, Englewood Cliffs (1993)
Brachmand, R.J., Levesque, H.J.: Knowledge Representation and Reasoning. MIT Press, Cambridge (2003)
Preece, A.D.: Validation of Knowledge-Based Systems: The State-of-the-Art in North America. J. Study of Artificial Intelligence Cognitive Science and Applied Epistomology 11(4) (1994)
Alpaydin, E.: Introduction to Machine Learning. In: Adaptive Computation and Machine Learning. MIT Press, Cambridge (2004)
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. In: Barbara, D., Jajodia, S. (eds.) Matrix Eigensystem Routines - EISPACK Guide. LNCS, vol. 6(4) (2002)
Williams, C.: A MCMC approach to hierarchical mixture modeling. Advances in Neural Information Processing Systems 12, 680–686 (2000)
Garrett-Mayer, E., Parmigiani, G.: Clustering and Classification Methods for Gene Expression Data Analysis. Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 70 (2004)
Yin, L., Huang, C.H., Ni, J.: Clustering of gene expression data: performance and similarity analysis. BMC Bioinformatics 7(suppl. 4), 19 (2006)
Kohonen, T.: Self-organizing maps. Springer, Berlin (1997)
Carrascal, A., Couchet, J., Ferreira, E., Manrique, D.: Anomaly Detection using prior knowledge: application to TCP/IP traffic. Artificial Intelligence in Theory and Practice - IFIP International Federation for Information Processing 217, 139–148 (2006)
Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering Applications of the Self-Organizing Map. Proceedings of the IEEE 84(10), 1358–1384 (1996)
Huang, S., Ward, M.O., Rundensteiner, E.A.: Exploration of dimensionality reduction for text visualization. Technical Report TR-03-14, Worcester Polytechnic Institute, Computer Science Department (2003)
Construcciones y Auxiliar de Ferrocarriles, http://www.caf.net/caste/home/index.php
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carrascal, A., Díez, A., Azpeitia, A. (2009). Unsupervised Methods for Anomalies Detection through Intelligent Monitoring Systems. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-02319-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02318-7
Online ISBN: 978-3-642-02319-4
eBook Packages: Computer ScienceComputer Science (R0)