Threat Modeling for Digital Twins
1.4 CASE STUDY UNDER CONSIDERATION
To illustrate the proposed method, we will consider the digital twin using vehicle telematics data for the commercial fleet maintenance( hereinafter FleetTwin for short). Its primary goals are to:
• Monitor real-time vehicle geolocation and health( e. g., engine performance, battery status, tire pressure).
• Predict mechanical failures using predictive analytics to minimize downtime.
• Optimize maintenance schedules by analyzing historical and real-time data.
• Reduce operational costs through proactive repairs and fuel efficiency insights.
• Enhance decision-making for fleet managers via actionable insights( e. g. provide the nearest car service location in case of possible breakdown, suggest that the driver needs to be replaced etc.).
The following components comprise FleetTwin system:
1. In-vehicle telematics devices. These are electronic control units( ECUs) embedded in vehicles to collect data( e. g. GPS location, engine diagnostics, fuel consumption, brake wear) and transmit data to the cloud via cellular / satellite networks.
2. Data Transmission Network: cellular network connection via private APN( Access Point Name) to provide dedicated network access, reduce exposure to public networks, and enable control over data traffic. Ensures connectivity between vehicles and the cloud platform.
3. Cloud Storage & Processing Platform, which stores raw telematics data( e. g. AWS, Azure, Outscale) and preprocesses data( cleaning, normalization) for analysis.
4. Digital Twin Engine, which generates and updates virtual vehicle models using real-time data and simulates scenarios( e. g. stress testing components, failure modes).
5. Analytics & Machine Learning( ML) Module to apply ML algorithms to detect anomalies and predict failures( e. g. engine breakdowns), integrate historical data, OEM specifications, and environmental factors( e. g. weather).
6. User Interface( Dashboard) based on web interface / mobile application for fleet managers to report vehicle location data, view vehicle health, alerts, and provide recommendations. Enables manual overrides( e. g. rerouting vehicles to the car service for urgent repairs).
7. Maintenance Management Integration component: automatically schedules repairs via integrations with enterprise systems( e. g. SAP, Oracle) and shares work orders with repair centers and parts suppliers.
8. Third-Party Services enabling external APIs for weather data, traffic updates, or OEM diagnostic tools.
For this case study, we don’ t consider feedback of digital twin system to the real world and possible safety impact. It is also assumed that all data from vehicles don’ t contain any private data. Generally, security issues are in focus for trustworthiness( see Figure 1-2).
Journal of Innovation 23