Digital Twin-based Intrusion Detection for Industrial Control Systems
| Autor: | Seba Anna Varghese, Alireza Dehlaghi Ghadim, Ali Balador, Zahra Alimadadi, Panos Papadimitratos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Ensemble models
FOS: Computer and information sciences Computer Science - Machine Learning Denial-of-service attack Computer Science - Cryptography and Security Stacked Ensemble Model Learning algorithms E-learning Machine Learning (cs.LG) Machine Learning Digital Twin Computer Systems medicinsk/hälsovetenskaplig inriktning Intrusion detection Security frameworks Machine-learning specialising in Medical and Health Sciences Industrial systems Industrial Control Systems Predictive maintenance Intrusion Detection Systems Gerontologi Intrusion-Detection Datorsystem Simulation optimization Gerontology Cryptography and Security (cs.CR) Supervised learning |
| Popis: | Digital twins have recently gained significant interest in simulation, optimization, and predictive maintenance of Industrial Control Systems (ICS). Recent studies discuss the possibility of using digital twins for intrusion detection in industrial systems. Accordingly, this study contributes to a digital twin-based security framework for industrial control systems, extending its capabilities for simulation of attacks and defense mechanisms. Four types of process-aware attack scenarios are implemented on a standalone open-source digital twin of an industrial filling plant: command injection, network Denial of Service (DoS), calculated measurement modification, and naive measurement modification. A stacked ensemble classifier is proposed as the real-time intrusion detection, based on the offline evaluation of eight supervised machine learning algorithms. The designed stacked model outperforms previous methods in terms of F1-Score and accuracy, by combining the predictions of various algorithms, while it can detect and classify intrusions in near real-time (0.1 seconds). This study also discusses the practicality and benefits of the proposed digital twin-based security framework. 7 pages, 7 figures, 3 tables, workshop paper |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|