Advancing Anomaly Detection in Time Series Data: a Knowledge Distillation Approach With Lstm Model
| dc.contributor.author | Kilinc,S. | |
| dc.contributor.author | Camlidere,B. | |
| dc.contributor.author | Yildiz,E. | |
| dc.contributor.author | Guler,A.K. | |
| dc.contributor.author | Alsan,H.F. | |
| dc.contributor.author | Arsan,T. | |
| dc.date.accessioned | 2024-06-23T21:39:20Z | |
| dc.date.available | 2024-06-23T21:39:20Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | This paper focuses on enhancing anomaly detection in time series data using deep learning techniques. Particularly, it investigates the integration of knowledge distillation with LSTM-based models for improved precision, efficiency, and interpretability. The study outlines objectives such as dataset preprocessing, developing a novel LSTM-knowledge distillation framework, incorporating Grafana, InfluxDB, Flask API with Docker, performance assessment, and practical implications. Results highlight the efficacy of knowledge distillation in enhancing student model performance. The proposed approach enhances anomaly detection, offering a viable solution for real-world applications. © 2023 IEEE. | en_US |
| dc.identifier.citationcount | 0 | |
| dc.identifier.doi | 10.1109/ASYU58738.2023.10296674 | |
| dc.identifier.isbn | 979-835030659-0 | |
| dc.identifier.scopus | 2-s2.0-85178280287 | |
| dc.identifier.uri | https://doi.org/10.1109/ASYU58738.2023.10296674 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12469/5856 | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 194153 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Anomaly Detection | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Knowledge Distillation | en_US |
| dc.subject | LSTM | en_US |
| dc.subject | Network Traffic | en_US |
| dc.subject | Time Series | en_US |
| dc.title | Advancing Anomaly Detection in Time Series Data: a Knowledge Distillation Approach With Lstm Model | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
| gdc.author.institutional | Arsan, Taner | |
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| gdc.description.department | Kadir Has University | en_US |
| gdc.description.departmenttemp | Kilinc S., Kadir Has University, Department of Computer Engineering, Istanbul, Turkey; Camlidere B., Turknet, Department of Data Science, Istanbul, Turkey; Yildiz E., Turknet, Department of Data Science, Istanbul, Turkey; Guler A.K., Kadir Has University, Department of Computer Engineering, Istanbul, Turkey; Alsan H.F., Kadir Has University, Department of Computer Engineering, Istanbul, Turkey; Arsan T., Kadir Has University, Department of Computer Engineering, Istanbul, Turkey | en_US |
| gdc.description.endpage | 6 | |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
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