Abstract
The growing complexity of vehicle network connectivity has
broadened the cyber-threat landscape, introducing substantial safety
risks for both passengers and the environment. Traditional security mechanisms, relying on rigid decision-making processes, often fail to address
the demands of this dynamic and interconnected ecosystem. To effectively manage emerging security threats and adapt to diverse scenarios,
integrating context awareness has become crucial. Context-aware systems can typically adapt their behaviour in response to changes in their
surrounding environment using context information. Ontologies serve
as powerful tools for modelling and reasoning of context information.
However, existing ontology-based context-aware security models are constrained by static thresholds and fail to adapt the rapid changes in real-time. This paper introduces a dynamic context-aware real-time security
model for the automotive domain. By leveraging a Python-based implementation alongside OWL 2 RL Ontology model, the proposed approach
dynamically adapts context information based on live data for security
analysis. The applicability and effectiveness of the proposed approach is
demonstrated using a use case of EV charging process.
broadened the cyber-threat landscape, introducing substantial safety
risks for both passengers and the environment. Traditional security mechanisms, relying on rigid decision-making processes, often fail to address
the demands of this dynamic and interconnected ecosystem. To effectively manage emerging security threats and adapt to diverse scenarios,
integrating context awareness has become crucial. Context-aware systems can typically adapt their behaviour in response to changes in their
surrounding environment using context information. Ontologies serve
as powerful tools for modelling and reasoning of context information.
However, existing ontology-based context-aware security models are constrained by static thresholds and fail to adapt the rapid changes in real-time. This paper introduces a dynamic context-aware real-time security
model for the automotive domain. By leveraging a Python-based implementation alongside OWL 2 RL Ontology model, the proposed approach
dynamically adapts context information based on live data for security
analysis. The applicability and effectiveness of the proposed approach is
demonstrated using a use case of EV charging process.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 21st International Conference on Computing and Information Technology |
| Publisher | Springer, Cham |
| Pages | 123-133 |
| Number of pages | 11 |
| Edition | 1 |
| ISBN (Electronic) | 978-3-031-90295-6 |
| ISBN (Print) | 978-3-031-90294-9 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 21st International Conference on Computing and Information Technology - Kanchanaburi, Thailand Duration: 15 May 2025 → 16 May 2025 |
Publication series
| Name | Lecture Notes in Networks and Systems |
|---|---|
| ISSN (Print) | 2367-3370 |
| ISSN (Electronic) | 2367-3389 |
Conference
| Conference | 21st International Conference on Computing and Information Technology |
|---|---|
| Abbreviated title | IC2IT 2025 |
| Country/Territory | Thailand |
| City | Kanchanaburi |
| Period | 15/05/25 → 16/05/25 |
Keywords
- Automotive cybersecurity
- Context-Aware Reasoning
- Ontology
- SWRL
- EV charging
- Real-Time Data Adaptation