Real-Time Adaptive Anomaly Detection in Industrial IoT Environments
Raeiszadeh, Mahsa ; Ebrahimzadeh, Amin ; Glitho, Roch ; Eker, Johan LU
and Mini, Raquel
(2024)
In IEEE Transactions on Network and Service Management
- Abstract
- To ensure reliability and service availability, nextgeneration networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in todays Industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model... (More)
- To ensure reliability and service availability, nextgeneration networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in todays Industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-theart anomaly detection methods by achieving up to an 89.71 accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements. (Less)
- Abstract (Swedish)
- To ensure reliability and service availability, nextgeneration networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in todays Industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model... (More)
- To ensure reliability and service availability, nextgeneration networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in todays Industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-theart anomaly detection methods by achieving up to an 89.71 accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/dae90928-55a2-4575-a9de-94df8461aecc
- author
- Raeiszadeh, Mahsa
; Ebrahimzadeh, Amin
; Glitho, Roch
; Eker, Johan
LU
and Mini, Raquel
- organization
- publishing date
- 2024-08-23
- type
- Contribution to journal
- publication status
- published
- subject
- in
- IEEE Transactions on Network and Service Management
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85201752324
- ISSN
- 1932-4537
- DOI
- 10.1109/TNSM.2024.3447532
- project
- AORTA: Advanced Offloading for Real-Time Applications
- language
- Swedish
- LU publication?
- yes
- id
- dae90928-55a2-4575-a9de-94df8461aecc
- date added to LUP
- 2024-09-02 10:14:01
- date last changed
- 2024-09-13 09:57:22
@article{dae90928-55a2-4575-a9de-94df8461aecc,
abstract = {{To ensure reliability and service availability, nextgeneration networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in todays Industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-theart anomaly detection methods by achieving up to an 89.71 accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements.}},
author = {{Raeiszadeh, Mahsa and Ebrahimzadeh, Amin and Glitho, Roch and Eker, Johan and Mini, Raquel}},
issn = {{1932-4537}},
language = {{swe}},
month = {{08}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Network and Service Management}},
title = {{Real-Time Adaptive Anomaly Detection in Industrial IoT Environments}},
url = {{http://dx.doi.org/10.1109/TNSM.2024.3447532}},
doi = {{10.1109/TNSM.2024.3447532}},
year = {{2024}},
}