Securing Internet of Vehicles IoV Communications: A Biometric and Hash-Key Derivation Function HKDF-Based Approach
In light of advancements in the Internet of Things (IoT), the Internet of Vehicles (IoV) has emerged as a crucial component in developing autonomous vehicles. However, the rapid evolution of this technology has introduced significant security concerns. This paper addresses the growing threat of cyberattacks on autonomous vehicles that utilize wireless communication networks. The proposed model introduces a novel solution to safeguard driver privacy and secure network connections during vehicle operation by employing the driver’s biometric information and Hash-Key Derivation Function (HKDF) (HMAC-based Key Derivation Function) to generate a unique session key. This approach fills a critical research gap by enhancing the security of automated vehicle communication systems vulnerable to various cyber threats. The results demonstrate the effectiveness of the proposed method in creating a robust and unpredictable network key, significantly increasing the difficulty for attackers in compromising the system.
Authors
- Mohammad Aljaidi (ORCID: https://orcid.org/0000-0001-9486-3533)
- Ghassan Samara (ORCID: https://orcid.org/0000-0001-8415-0572)
- Hamzeh Almomani (ORCID: https://orcid.org/0000-0002-4537-5477)
- Ayoub Alsarhan (ORCID: https://orcid.org/0000-0001-9075-2828)
- Tamam Alsarhan (ORCID: https://orcid.org/0000-0002-0323-0464)
- Bashar Khassawneh (ORCID: https://orcid.org/0009-0000-4647-0460)
- Manish Kumar Singla (ORCID: https://orcid.org/0000-0003-1028-2729)
- Mahmoud AlJamal (ORCID: https://orcid.org/0009-0007-5389-6778)
- Abdelrahman Alfaqih
Institutions
- Amman Arab University (JO)
- Zarqa University (JO)
- Hashemite University (JO)
- University of Business and Technology (SA)
- Chitkara University (IN)
- Irbid National University (JO)
Publication Details
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1109/acit62805.2024.10877191
- Citations
- 7
- Primary Topic
- Biometric Identification and Security
- Type
- article
- Field-Weighted Citation Impact
- 2.45