Hussain, Moaz Tajammal and Perera, Charith  ORCID: https://orcid.org/0000-0002-0190-3346
2022.
Explainable sensor data-driven anomaly detection in Internet of Things systems.
Presented at: 7th ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI 2022),
Milan, Italy,
3-6 May 2022.
2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI).
IEEE,
pp. 80-81.
10.1109/IoTDI54339.2022.00021
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Official URL: https://doi.org/10.1109/IoTDI54339.2022.00021
Abstract
Deep learning or black-box models are widely used for anomaly detection in Internet of Things (IoT) data streams. We propose a technique to explain the output of a deep learning model used to detect anomalies in an IoT based industrial process. The proposed technique employs dual surrogate models to deliver black box model explanation. We have also developed an interactive dashboard to give further insights into the detected anomaly. The dashboard integrates our proposed deep learning explanation technique with historical logs to explain the detected anomaly for personas with different backgrounds.
| Item Type: | Conference or Workshop Item - published (Poster) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics Schools > Mathematics |
| Subjects: | T Technology > T Technology (General) |
| Publisher: | IEEE |
| ISBN: | 978-1-6654-9642-1 |
| Date of First Compliant Deposit: | 9 March 2022 |
| Last Modified: | 01 Jul 2026 10:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/148043 |
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