A Context-Aware Real-Time Security Model for Automotive Systems

Teena Kumari; Abdur Rakib; Arkady Zaslavsky; Hesam Jadidbonab; Valeh Moghaddam

doi:10.1007/978-3-031-90295-6_13

A Context-Aware Real-Time Security Model for Automotive Systems

Teena Kumari
, Abdur Rakib
, Arkady Zaslavsky
, Hesam Jadidbonab
, Valeh Moghaddam

Centre for Future Transport and Cities

Deakin University

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

4 Downloads (Pure)

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.

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	https://doi.org/10.1007/978-3-031-90295-6_13
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

Bibliographical note

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright
owners. A copy can be downloaded for personal non-commercial research or study,
without prior permission or charge. This item cannot be reproduced or quoted
extensively from without first obtaining permission in writing from the copyright
holder(s). The content must not be changed in any way or sold commercially in any
format or medium without the formal permission of the copyright holders.
This version of the article has been accepted for publication, after peer review (when
applicable) but is not the Version of Record and does not reflect post-acceptance
improvements, or any corrections. The Version of Record is available online at:
http://dx.doi.org/10.1007/978-3-031-90295-6_13

Keywords

Automotive cybersecurity
Context-Aware Reasoning
Ontology
SWRL
EV charging
Real-Time Data Adaptation

Access to Document

10.1007/978-3-031-90295-6_13

Kumari2025AAMAccepted author manuscript, 1.06 MB

Cite this

A Context-Aware Real-Time Security Model for Automotive Systems. / Kumari, Teena ; Rakib, Abdur; Zaslavsky, Arkady et al.
Proceedings of the 21st International Conference on Computing and Information Technology . 1. ed. Springer, Cham , 2025. p. 123-133 (Lecture Notes in Networks and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Kumari, T , Rakib, A, Zaslavsky, A, Jadidbonab, H & Moghaddam, V 2025, A Context-Aware Real-Time Security Model for Automotive Systems. in Proceedings of the 21st International Conference on Computing and Information Technology . 1 edn, Lecture Notes in Networks and Systems, Springer, Cham , pp. 123-133, 21st International Conference on Computing and Information Technology, Kanchanaburi, Thailand, 15/05/25. https://doi.org/10.1007/978-3-031-90295-6_13

@inproceedings{633b9627d46d4f378d061cdfc2cf0f1e,

title = "A Context-Aware Real-Time Security Model for Automotive Systems",

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.",

keywords = "Automotive cybersecurity, Context-Aware Reasoning, Ontology, SWRL, EV charging, Real-Time Data Adaptation",

author = "Teena Kumari and Abdur Rakib and Arkady Zaslavsky and Hesam Jadidbonab and Valeh Moghaddam",

note = "Copyright {\textcopyright} and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-90295-6\_13; 21st International Conference on Computing and Information Technology, IC2IT 2025 ; Conference date: 15-05-2025 Through 16-05-2025",

year = "2025",

doi = "10.1007/978-3-031-90295-6\_13",

language = "English",

isbn = "978-3-031-90294-9",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer, Cham ",

pages = "123--133",

booktitle = "Proceedings of the 21st International Conference on Computing and Information Technology",

address = "Switzerland",

edition = "1",

}

TY - GEN

T1 - A Context-Aware Real-Time Security Model for Automotive Systems

AU - Kumari, Teena

AU - Rakib, Abdur

AU - Zaslavsky, Arkady

AU - Jadidbonab, Hesam

AU - Moghaddam, Valeh

N1 - Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-90295-6_13

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Automotive cybersecurity

KW - Context-Aware Reasoning

KW - Ontology

KW - SWRL

KW - EV charging

KW - Real-Time Data Adaptation

U2 - 10.1007/978-3-031-90295-6_13

DO - 10.1007/978-3-031-90295-6_13

M3 - Conference proceeding

SN - 978-3-031-90294-9

T3 - Lecture Notes in Networks and Systems

SP - 123

EP - 133

BT - Proceedings of the 21st International Conference on Computing and Information Technology

PB - Springer, Cham

T2 - 21st International Conference on Computing and Information Technology

Y2 - 15 May 2025 through 16 May 2025

ER -

A Context-Aware Real-Time Security Model for Automotive Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this