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Anomaly Detection Using LSTM-Autoencoder in Smart Grid: A Federated Learning Approach
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-8470-3277
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-3719-7295
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0001-5951-9374
Tst, Spain.
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2023 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2023, p. 48-54Conference paper, Published paper (Refereed)
Abstract [en]

ABSTRACT. Anomaly detection is critical in industrial systems such as smart grid systems to guarantee their safe and effective operation. The smart grid stations contain sensitive data, and they are concerned about sharing it with a third-party server to establish a centralized anomaly detection system. Federated Learning (FL) is a feasible solution to these problems for enhancing anomaly detection in smart grid systems. This study describes a method for developing an unsupervised anomaly detection based on FL system using a synthetic dataset based on real-world grid system behavior. The paper investigates the usage of FL’s long short-term memory autoencoder (LSTM-AE) for anomaly detection. For more accurate identification, this research explores the performance of integrating LSTM-AE with one-class support vector machine (OC-SVM) and isolation forest (IF) and compares their results with a threshold-based anomaly detection approach. Moreover, an approach is described for generating synthetic anomalies with different levels of difficulty to evaluate the robustness of the anomaly detection FL model. The FL models results are compared with the centralized version of the models as a baseline and the results show that FL models outperformed the centralized approach by detecting higher outlier data by achieving 99% F1-Score.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2023. p. 48-54
Keywords [en]
Anomaly detection; Learning systems; Long short-term memory; Sensitive data; Smart power grids; Additional key word and phrase: autoencoder; Anomaly detection; Auto encoders; Federated learning; Isolation forest; Key words; Key-phrase; LSTM; One-class support vector machine; Smart grid; Support vectors machine; Support vector machines
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-67956DOI: 10.1145/3616131.3616138Scopus ID: 2-s2.0-85176004408OAI: oai:DiVA.org:ri-67956DiVA, id: diva2:1814664
Conference
7th International Conference on Cloud and Big Data Computing, ICCBDC 2023. Manchester, UK. 17 August 2023 through 19 August 2023
Note

This work was partially supported by EU ECSEL project DAIS that has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No.101007273

Available from: 2023-11-27 Created: 2023-11-27 Last updated: 2023-12-27Bibliographically approved

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Mohammadi, MohammadrezaShrestha, RakeshSinaei, Sima

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