Title :

Federated Learning-Based Explainable Anomaly Detection for Industrial Control Systems

Author(s) :

Huong, T. T. [Auteur]
Bac, T. P. [Auteur]
Ha, K. N. [Auteur]
Hoang, N. V. [Auteur]
Hoang, N. X. [Auteur]
Hung, N. T. [Auteur]
Tran, Kim-Phuc [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]

Journal title :

IEEE Access

Abbreviated title :

IEEE ACCESS

Volume number :

10

Pages :

3854-53872

Publication date :

2022-05-09

ISSN :

2169-3536

English keyword(s) :

Anomaly detection
Integrated circuits
Training
Industrial Internet of Things
Computational modeling
Support vector machines
Edge computing
Anomaly detection
ICS
federated learning
XAI
VAE
SVDD

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

We are now witnessing the rapid growth of advanced technologies and their application, leading to Smart Manufacturing (SM). The Internet of Things (IoT) is one of the main technologies used to enable smart factories, which ...
Show more >We are now witnessing the rapid growth of advanced technologies and their application, leading to Smart Manufacturing (SM). The Internet of Things (IoT) is one of the main technologies used to enable smart factories, which is connecting all industrial assets, including machines and control systems, with the information systems and the business processes. Industrial Control Systems of smart IoT-based factories are one of the top industries attacked by numerous threats, especially unknown and novel attacks. As a result, with the distributed structure of plenty of IoT front-end sensing devices in SM, an effectively distributed anomaly detection (AD) architecture for IoT-based ICSs should: achieve high detection performance, train and learn new data patterns in a fast time scale, and have lightweight to be deployed on resource-constrained edge devices. To date, most solutions for anomaly detection have not fulfilled all of these requirements. In addition, the interpretability of why an instance is predicted to be abnormal is hardly concerned. In this paper, we propose the so- called FedeX architecture to address those challenges. The experiments show that FedeX outperforms 14 other existing anomaly detection solutions on all detection metrics with the liquid storage data set. And with Recall of 1 and F1-score of 0.9857, it also outperforms those solutions on the SWAT data set. FedeX is also proven to be fast in terms of training time of about 7.5 minutes and lightweight in terms of hardware requirement with memory consumption of 14%, allowing us to deploy anomaly detection tasks on top of edge computing infrastructure and in real-time. Besides, FedeX is considered as one of the frameworks at the forefront of interpreting the predicted anomalies by using XAI, which enables experts to make quick decisions and trust the model more.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

2023-06-20T12:04:54Z
2024-02-21T16:45:32Z