TY - JOUR
T1 - Enhancing Intelligent Transport Systems Through Decentralized Security Frameworks in Vehicle-to-Everything Networks
AU - Tariq, Usman
AU - Ahanger, Tariq Ahamed
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/1
Y1 - 2025/1
N2 - Vehicle Ad hoc Networks (VANETs) play an essential role in intelligent transportation systems (ITSs) by improving road safety and traffic management through robust decentralized communication between vehicles and infrastructure. Yet, decentralization introduces security vulnerabilities, including spoofing, tampering, and denial-of-service attacks, which can compromise the reliability and safety of vehicular communications. Traditional centralized security mechanisms are often inadequate in providing the real-time response and scalability required by such dispersed networks. This research promotes a shift toward distributed and real-time technologies, including blockchain and secure multi-party computation, to enhance communication integrity and privacy, ultimately strengthening system resilience by eliminating single points of failure. A core aspect of this study is the novel D-CASBR framework, which integrates three essential components. First, it employs hybrid machine learning methods, such as ElasticNet and Gradient Boosting, to facilitate real-time anomaly detection, identifying unusual activities as they occur. Second, it utilizes a consortium blockchain to provide secure and transparent information exchange among authorized participants. Third, it implements a fog-enabled reputation system that uses distributed fog computing to effectively manage trust within the network. This comprehensive approach addresses latency issues found in conventional systems while significantly improving the reliability and efficacy of threat detection, achieving 95 percent anomaly detection accuracy with minimal false positives. The result is a substantial advancement in securing vehicular networks.
AB - Vehicle Ad hoc Networks (VANETs) play an essential role in intelligent transportation systems (ITSs) by improving road safety and traffic management through robust decentralized communication between vehicles and infrastructure. Yet, decentralization introduces security vulnerabilities, including spoofing, tampering, and denial-of-service attacks, which can compromise the reliability and safety of vehicular communications. Traditional centralized security mechanisms are often inadequate in providing the real-time response and scalability required by such dispersed networks. This research promotes a shift toward distributed and real-time technologies, including blockchain and secure multi-party computation, to enhance communication integrity and privacy, ultimately strengthening system resilience by eliminating single points of failure. A core aspect of this study is the novel D-CASBR framework, which integrates three essential components. First, it employs hybrid machine learning methods, such as ElasticNet and Gradient Boosting, to facilitate real-time anomaly detection, identifying unusual activities as they occur. Second, it utilizes a consortium blockchain to provide secure and transparent information exchange among authorized participants. Third, it implements a fog-enabled reputation system that uses distributed fog computing to effectively manage trust within the network. This comprehensive approach addresses latency issues found in conventional systems while significantly improving the reliability and efficacy of threat detection, achieving 95 percent anomaly detection accuracy with minimal false positives. The result is a substantial advancement in securing vehicular networks.
KW - decentralized intrusion detection systems (D-IDSs)
KW - fog computing
KW - hybrid machine learning
KW - interconnectivity
KW - minority class imbalance
KW - privacy preserving
KW - real-time data processing
KW - vehicular ad hoc networks (VANETs)
UR - http://www.scopus.com/inward/record.url?scp=85215781102&partnerID=8YFLogxK
U2 - 10.3390/wevj16010024
DO - 10.3390/wevj16010024
M3 - Article
AN - SCOPUS:85215781102
SN - 2032-6653
VL - 16
JO - World Electric Vehicle Journal
JF - World Electric Vehicle Journal
IS - 1
M1 - 24
ER -