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An Anomaly Detection and Explainability Framework using Convolutional Autoencoders for Data Storage Systems
Open accessAbstract
Anomaly detection in data storage systems is a challenging problem due to the high dimensional sequential data involved, and lack of labels. The state of the art for automating anomaly detection in these systems typically relies on hand crafted rules and thresholds which mainly allow to distinguish between normal and abnormal behavior of each indicator in isolation. In this work we present an end-to-end framework based on convolutional autoencoders which not only allows for anomaly detection on multivariate time series data, but also provides explainability. This is done by identifying similar historic anomalies and extracting the most influential indicators. These are then presented to relevant personnel such as system designers and architects, or to support engineers for further analysis. We demonstrate the application of this framework along with an intuitive interactive web interface which was developed for data storage system anomaly detection. We discuss how this framework along with its explainability aspects enables support engineers to effectively tackle abnormal behaviors, all while allowing for crucial feedback.
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“Second, nearly all deep post-model XAD techniques provide only feature-based explanations; the only exceptions are References [14,48,72], which also produce sample-based explanations.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [14]
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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A Survey on Explainable Anomaly Detection
Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139
Metadata
- Authors
- Assaf, R., Giurgiu, I., Pfefferle, J., Monney, S., Pozidis, H., & Schumann, A.
- Affiliations
- IBM Research - Zurich (CH)
- IBM
- Venue
- Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
- Published
- 2020-07
- Pages
- 5228-5230
- OpenAlex
- W3040717095
- Cited by
- 7
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- anomaly detection, data storage systems, convolutional autoencoder, explainability, multivariate time series, support engineering, interactive web interface, unsupervised learning
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- Assaf, R., Giurgiu, I., Pfefferle, J., Monney, S., Pozidis, H., & Schumann, A. (2020). An Anomaly Detection and Explainability Framework using Convolutional Autoencoders for Data Storage Systems. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2020/752
