Title :

Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach

Author(s) :

Huong, T. T. [Auteur]
Bac, T. P. [Auteur]
Long, D. M. [Auteur]
Luong, T. D. [Auteur]
Dan, N. M. [Auteur]
Quang, L. [Auteur]
Cong, L. T. [Auteur]
Thang, B. D. [Auteur]
Tran, Kim-Phuc [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]

Journal title :

Computers in Industry

Abbreviated title :

Comput. Ind.

Volume number :

132

Pages :

-

Publication date :

2021-10-05

ISSN :

0166-3615

English keyword(s) :

IIoT
Federated Learning
Time series
Anomaly detection
Industrial control systems
Smart manufacturing

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

In recent years, the rapid development and wide application of advanced technologies have profoundly impacted industrial manufacturing, leading to smart manufacturing (SM). However, the Industrial IoT (IIoT)-based manufacturing ...
Show more >In recent years, the rapid development and wide application of advanced technologies have profoundly impacted industrial manufacturing, leading to smart manufacturing (SM). However, the Industrial IoT (IIoT)-based manufacturing systems are now one of the top industries targeted by a variety of attacks. In this research, we propose detecting Cyberattacks in Industrial Control Systems using Anomaly Detection. An anomaly detection architecture for the IIoT-based SM is proposed to deploy one of the top most concerned networking technique - a Federated Learning architecture - that can detect anomalies for time series data typically running inside an industrial system. The architecture achieves higher detection performance compared to the current detection solution for time series data. It also shows the feasibility and efficiency to be deployed on top of edge computing hardware of an IIoT-based SM that can save 35% of bandwidth consumed in the transmission link between the edge and the cloud. At the expense, the architecture needs to trade off with the computing resource consumed at edge devices for implementing the detection task. However, findings in maximal CPU usage of 85% and average Memory usage of 37% make this architecture totally realizable in an IIoT-based SM.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

2023-06-20T11:55:07Z
2024-03-21T10:00:34Z