Event and Anomaly Detection Using Tucker3 Decomposition

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

Hadi Fanaee-T (Autor) Outra; Márcia Oliveira (Autor) Outra; João Gama (Autor) FEP; Simon Malinowski (Autor) Outra; Ricardo Morla (Autor) FEUP

Outras Informações

ID Authenticus: P-00G-6BT

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. Often, network devices are not able to provide information about the type of failure. In such cases the type of failure is not known in advance and the unsupervised setting is more appropriate for diagnosis. Among unsupervised approaches, Principal Component Analysis (PCA) is a well-known solution which has been widely used in the 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11