Abstract
The objective of this paper is to present an application of learning algorithms to the detection of anomalies in SOA system. As it was not possible to inject errors into the "real" SOA system and to analyze the effect of these errors, a special model of SOA system was designed and implemented. In this system several anomalies were introduced and the effectiveness of algorithms in detecting them were measured. The results of experiments can be used to select efficient algorithm for anomaly detection. Two algorithms: K-means clustering and Kohonen networks were used to detect the unused functionalities and the results of this experiment are discussed. © 2013 IFIP International Federation for Information Processing.
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CITATION STYLE
Bluemke, I., & Tarka, M. (2013). Learning algorithms in the detection of unused functionalities in SOA systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8104 LNCS, pp. 389–400). https://doi.org/10.1007/978-3-642-40925-7_36
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