The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Aiden: Association-Learning-Based Attack Identification on the Edge of V2X Communication Networks
Open accessAbstract
In vehicle security, attack identification has been proposed to identify the compromised electronic control units (ECUs) of a vehicle. Fingerprinting methods using a variety of features have been widely applied to identify attacks. However, these methods only consider the features of an individual ECU, and ignore the logical association among different ECUs. This condition leads to high requirements in terms of feature measurements, and a great deal of useful information is lost to achieve identification. In this paper, an association-learning-based model, designated Aiden, is proposed to identify the compromised ECUs on the edge of V2X communication networks and without feature measurements. Experiments on a real vehicle show the effectiveness of the proposed model.
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“These practical implementations serve as effective countermeasures to fortify the security posture of the hardware infrastructure, ensuring a resilient and protected environment for V2X communication.”
From Towards 6G V2X Sidelink: Survey of Resource Allocation—Mathematical Formulations, Challenges, and Proposed Solutions (2024) · cites this paper as [133]
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Cites
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A Blockchain-SDN-Enabled Internet of Vehicles Environment for Fog Computing and 5G Networks
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Cited by
Sorted by citations
Annu, & Rajalakshmi, P. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3368240 · Cited by 73
Metadata
- Authors
- Chen, Y., Alam, M., & Mumtaz, S.
- Affiliations
- Hangzhou Dianzi University (CN)
- London South Bank University (GB)
- University of Aveiro (PT)
- Venue
- IEEE Transactions on Green Communications and Networking
- Published
- 2022-09
- Volume
- 6
- Issue
- 3
- Pages
- 1377-1385
- ISSN
- 2473-2400 (ISSN-L)
- OpenAlex
- W4285298118
- Cited by
- 10
- References
- 2
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 3 · 2022: 1
- FWCI
- 0.68
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- IEEE Transactions on Green Communications and Networking (publishedVersion)
- Research Open (London South Bank University) (acceptedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (61802097)
- Project of Qianjiang Talent (QJD1802020)
- Key Research and Development Plan of Zhejiang Province (2019C01012)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Keywords
- attack detection, automotive cybersecurity, vehicle-to-everything communication, electronic control units, associative learning, edge computing, fingerprinting, in-vehicle networks
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