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GNN-based Advanced Feature Integration for ICS Anomaly Detection

Authors:

Bailing WangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 6

Article No.: 106, Pages 1 - 32

https://doi.org/10.1145/3620676

Published: 14 November 2023 Publication History

Abstract

Recent adversaries targeting the Industrial Control Systems (ICSs) have started exploiting their sophisticated inherent contextual semantics such as the data associativity among heterogeneous field devices. In light of the subtlety rendered in these semantics, anomalies triggered by such interactions tend to be extremely covert, hence giving rise to extensive challenges in their detection. Driven by the critical demands of securing ICS processes, a Graph-Neural-Network (GNN) based method is presented to tackle these subtle hostilities by leveraging an ICS’s advanced contextual features refined from a universal perspective, rather than exclusively following GNN’s conventional local aggregation paradigm. Specifically, we design and implement the Graph Sample-and-Integrate Network (GSIN), a general chained framework performing node-level anomaly detection via advanced feature integration, which combines a node’s local awareness with the graph’s prominent global properties extracted via process-oriented pooling. The proposed GSIN is evaluated on multiple well-known datasets with different kinds of integration configurations, and results demonstrate its superiority consistently on not only anomaly detection performance (e.g., F1 score and AUPRC) but also runtime efficiency over recent representative baselines.

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Cited By

Alrumaih TAlenazi M(2024)CGAAD: Centrality- and Graph-Aware Deep-Learning Model for Detecting Cyberattacks Targeting Industrial Control Systems in Critical InfrastructureIEEE Internet of Things Journal10.1109/JIOT.2024.339069111:13(24162-24182)Online publication date: 1-Jul-2024
https://doi.org/10.1109/JIOT.2024.3390691

Index Terms

GNN-based Advanced Feature Integration for ICS Anomaly Detection

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Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 6

December 2023

493 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3632517

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2023

Online AM: 05 September 2023

Accepted: 21 August 2023

Revised: 27 June 2023

Received: 21 November 2022

Published in TIST Volume 14, Issue 6

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National Key R&D Program of China
National Natural Science Foundation of China
Double First-Class Scientific Research Funds of HIT

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Cited By

Alrumaih TAlenazi M(2024)CGAAD: Centrality- and Graph-Aware Deep-Learning Model for Detecting Cyberattacks Targeting Industrial Control Systems in Critical InfrastructureIEEE Internet of Things Journal10.1109/JIOT.2024.339069111:13(24162-24182)Online publication date: 1-Jul-2024
https://doi.org/10.1109/JIOT.2024.3390691

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