Cyber-Twin: Digital Twin-boosted Autonomous Attack Detection for Vehicular Ad-Hoc Networks
CoRR(2024)
摘要
The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a
transformative era for intelligent transportation systems (ITS), significantly
enhancing road safety and vehicular communication. However, the intricate and
dynamic nature of VANETs presents formidable challenges, particularly in
vehicle-to-infrastructure (V2I) communications. Roadside Units (RSUs), integral
components of VANETs, are increasingly susceptible to cyberattacks, such as
jamming and distributed denial-of-service (DDoS) attacks. These vulnerabilities
pose grave risks to road safety, potentially leading to traffic congestion and
vehicle malfunctions. Current approaches often struggle to effectively merge
digital twin technology with Artificial Intelligence (AI) models to boost
security and sustainability. Our study introduces an innovative cyber-twin
framework tailored to enhance the security of RSUs in VANETs. This framework
uniquely combines digital twin technology with cutting-edge AI to offer a
real-time, dynamic representation of RSUs. This allows for detailed monitoring
and efficient detection of threats, significantly strengthening RSU security in
VANETs. Moreover, our framework makes a notable contribution to eco-friendly
communication by improving the computational efficiency of RSUs, leading to
increased energy efficiency and extended hardware durability. Our results show
a considerable enhancement in resource management and attack detection,
surpassing the performance of existing solutions. In particular, the cyber-twin
framework showed a substantial reduction in RSU load and an optimal balance
between resource consumption and high attack detection efficiency, with a
defined twinning rate range of seventy-six to ninety per cent. These
advancements underscore our commitment to developing sustainable, secure, and
resilient vehicular communication systems for the future of smart cities.
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