Event and anomaly detection using Tucker3 decomposition

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Hadi Fanaee Tork (Author) Other; Márcia Oliveira (Author) Other; João Gama (Author) FEUP; Simon Malinowski (Author) Other; Ricardo Morla (Author) FEUP

Conference proceedings International

Title: CEUR Workshop Proceedings

Pages: 8-12

Workshop on Ubiquitous Data Mining, UDM 2012 - In Conjunction with the 20th European Conference on Artificial Intelligence, ECAI 2012

27 August 2012 through 31 August 2012

Indexing

Scopus - 6 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-00A-9RG

Abstract (EN): Failure detection in telecommunication networks is a vital task. So far, several supervised and unsupervised solutions have been provided for discovering failures in such networks. Among them unsupervised approaches has attracted more attention since no label data is required [1]. Often, network devices are not able to provide information about the type of failure. In such cases, unsupervised setting is more appropriate for diagnosis. Among unsupervised approaches, Principal Component Analysis (PCA) has been widely used for anomaly detection literature and can be applied to matrix data (e.g. Users-Features). However, one of the important properties of network data is their temporal sequential nature. So considering the interaction of dimensions over a third dimension, such as time, may provide us better insights into the nature of network failures. In this paper we demonstrate the power of three-way analysis to detect events and anomalies in time-evolving network data.

Language: English

Type (Professor's evaluation): Scientific