Event and Anomaly Detection Using Tucker3 Decomposition
This work addresses failure detection in telecommunication networks, which is vital for network operators, but it is incremental as it extends existing unsupervised methods to three-way data.
The paper tackled the problem of failure detection in telecommunication networks by applying Tucker3 decomposition to time-evolving network data, demonstrating its effectiveness for event and anomaly detection.
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.