research-article

Analysis of Deep Learning Models for Anomaly Detection in Time Series IoT Sensor Data

Authors:

Ujjwal Sachdeva,

Potukuchi Raghu VamsiAuthors Info & Claims

IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing

Pages 54 - 62

https://doi.org/10.1145/3549206.3549218

Published: 24 October 2022 Publication History

Abstract

The anomaly detection in Internet of Things (IoT) sensor data has become an important research area because of the possibility of noise and unavailability of labels in the sensors readings. The conventional machine learning algorithms cannot detect the anomalies when there is high correlation between the data points of the sensor data. Further, the volume and velocity of the data generated by the sensors in the IoT also a reason that the conventional statistical and machine learning algorithms fails to detect the anomalies. In recent years, the Deep Learning (DL) is gaining significant attention in the anomaly detection research due to the property of unsupervised learning of the high volume data and high detection accuracy of abnormalities. To this end, this paper proposed to study three DL models such as Autoencoders, Long Short Term Memory (LSTM) Autoencoder, and LSTM Recurrent Neural Networks (LSTM-RNN) for detecting anomalies in time series IoT sensor data. Simulations have been conducted using the Intel Berkeley Research Labs (IBRL) Sensor data to evaluate the performance. The results reveal which method performed better in terms of detection accuracy and training time.

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Cited By

Yakoi PMeng XCui SSuleman DYang X(2023)Analysis of Time Series Data Generated From the Internet of Things Using Deep Learning ModelsIEEE Access10.1109/ACCESS.2023.333176211(133313-133328)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3331762
Sachdeva UVamsi P(2022)A Study on Anomaly Detection with Deep Learning Models for IoT Time Series Sensor Data2022 8th International Conference on Signal Processing and Communication (ICSC)10.1109/ICSC56524.2022.10009580(11-14)Online publication date: 1-Dec-2022
https://doi.org/10.1109/ICSC56524.2022.10009580

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Information & Contributors

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Published In

cover image ACM Other conferences

IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing

August 2022

710 pages

ISBN:9781450396752

DOI:10.1145/3549206

General Chairs:
Sartaj Sahni
University of Florida, USA
,
Vikas Saxena
JIIT Noida, India
,
Program Chair:
Sundaraja Sitharama Iyengar
Florida International University, USA

Copyright © 2022 ACM.

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Publication History

Published: 24 October 2022

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IC3-2022: 2022 Fourteenth International Conference on Contemporary Computing

August 4 - 6, 2022

Noida, India

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Cited By

Yakoi PMeng XCui SSuleman DYang X(2023)Analysis of Time Series Data Generated From the Internet of Things Using Deep Learning ModelsIEEE Access10.1109/ACCESS.2023.333176211(133313-133328)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3331762
Sachdeva UVamsi P(2022)A Study on Anomaly Detection with Deep Learning Models for IoT Time Series Sensor Data2022 8th International Conference on Signal Processing and Communication (ICSC)10.1109/ICSC56524.2022.10009580(11-14)Online publication date: 1-Dec-2022
https://doi.org/10.1109/ICSC56524.2022.10009580

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