A Framework for Tunable Anomaly Detection

Alam, Rakibul; Gerostathopoulos, Ilias; Prehofer, Christian; Attanasi, Alessandro; Bureš, Tomáš

doi:10.1109/icsa.2019.00029

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Zhao et al [22] presented an adaptive open set domain generalisation network using local class Clustering-based representation learning and class-wide decision boundary-based outlier detection. In [23], a simple yet robust way to detect anomalies in arbitrary time series by detecting seasonal patterns and identifying critical anomaly thresholds was presented. A meta-framework to create unsupervised anomaly detectors was introduced by [24].…”

Section: Adaptive Anomaly Detection Methodsmentioning

confidence: 99%

Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems

Bieber,

Verhagen,

Cosson

et al. 2023

Aerospace

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Spacecraft systems collect health-related data continuously, which can give an indication of the systems’ health status. While they rarely occur, the repercussions of such system anomalies, faults, or failures can be severe, safety-critical and costly. Therefore, the data are used to anticipate any kind of anomalous behaviour. Typically this is performed by the use of simple thresholds or statistical techniques. Over the past few years, however, data-driven anomaly detection methods have been further developed and improved. They can help to automate the process of anomaly detection. However, it usually is time intensive and requires expertise to identify and implement suitable anomaly detection methods for specific systems, which is often not feasible for application at scale, for instance, when considering a satellite consisting of numerous systems and many more subsystems. To address this limitation, a generic diagnostic framework is proposed that identifies optimal anomaly detection techniques and data pre-processing and thresholding methods. The framework is applied to two publicly available spacecraft datasets and a real-life satellite dataset provided by the European Space Agency. The results show that the framework is robust and adaptive to different system data, providing a quick way to assess anomaly detection for the underlying system. It was found that including thresholding techniques significantly influences the quality of resulting anomaly detection models. With this, the framework can provide both a way forward in developing data-driven anomaly detection methods for spacecraft systems and guidance relative to the direction of anomaly detection method selection and implementation for specific use cases.

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Section: Adaptive Anomaly Detection Methodsmentioning

confidence: 99%

Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems

Bieber,

Verhagen,

Cosson

et al. 2023

Aerospace

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“…In this case, the performance of the method is highly limited by the patterns defined for each KPI by network experts. These experts also have an important role in the generic system proposed in [ 12 ]. This system detects anomalies in real time, but its configuration must be constantly tuned using the feedback provided by experts in order to reach a good performance over time, increasing network OPEX.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, it is mandatory that the deployed technique is updated over time without any interruption to continue operating in a feasible manner. This is a major constraint for some techniques that must be retrained with new labeled data or for other approaches that must be manually tuned [ 12 ]. This implies a significant showstopper for these techniques to be deployed through a mobile network with a large number of nodes.…”

Section: Introductionmentioning

confidence: 99%

Online Anomaly Detection System for Mobile Networks

Burgueño

de-la-Bandera

Mendoza

et al. 2020

Sensors

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The arrival of the fifth generation (5G) standard has further accelerated the need for operators to improve the network capacity. With this purpose, mobile network topologies with smaller cells are currently being deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the number of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automatically inform the network operator of the relevant information within the vast amount of metrics collected. The continuous monitoring of the performance indicators and the automatic detection of anomalies is especially important for network operators to prevent the network degradation and user complaints. Therefore, this paper proposes a methodology to detect and track anomalies in the mobile networks performance indicators online, i.e., in real time. The feasibility of this system was evaluated with several performance metrics and a real LTE Advanced dataset. In addition, it was also compared with the performances of other state-of-the-art anomaly detection systems.

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