An Explainable Deep Learning-based Approach for Multivariate Time Series Anomaly Detection in IoT-Reference-Cited by-同舟云学术

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An Explainable Deep Learning-based Approach for Multivariate Time Series Anomaly Detection in IoT

Published:2023-12-11 Issue: Volume: Page:108-113
ISSN:
Container-title:2023 International Conference on Frontiers of Information Technology (FIT)
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Author:

Toor Aafan Ahmad¹,Lin Jia-Chun¹,Gran Ernst Gunnar¹,Lee Ming-Chang²

Affiliation:

1. Norwegian University of Science and Technology (NTNU),Department of Information Security and Communication Technology,Gjøvik,Norway

2. Høgskulen på Vestlandet (HVL),Electrical Engineering and Mathematical Sciences,Department of Computer Science,Bergen,Norway

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10410269/10410290/10410343.pdf?arnumber=10410343

Reference21 articles.

1. Deep Learning-Based Time-Series Analysis for Detecting Anomalies in Internet of Things

2. LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data

3. Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset

4. An explainable deep learning-enabled intrusion detection framework in IoT networks

5. SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series

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