Browse Publications Technical Papers 2024-28-0038

Contextual Study of Security and Privacy in V2X Communication for Architecture & Networking products 2024-28-0038

In recent times there has been an upward trend in "Connected Vehicles", which has significantly improved not only the driving experience but also the "ownership of the car". The use of state-of-the-art wireless technologies, such as vehicle-to-everything (V2X) connectivity, is crucial for its dependability and safety. V2X also effectively extends the information flow between the transportation ecosystem pedestrians, public infrastructure (traffic management system) and parking infrastructure, charging and fuel stations, Etc. V2X has a lot of potential to enhance traffic flow, boost traffic safety, and provide drivers and operators with new services. One of the fundamental issues is maintaining trustworthy and quick communication between cars and infrastructure. While establishing stable connectivity, reducing interference, and controlling the fluctuating quality of wireless transmissions, we have to ensure the Security and Privacy of V2I. Since there are multiple and diverse stakeholders in the V2I development, harmonizing standards and security protocols become crucial, to ensure scalability and interoperability. To address these issues, the authors have surveyed into a landscape of security solutions that can possibly be implemented to reduce the challenges associated with V2X such as Secure Communication Protocols (TLS, DTLS), Cryptography-based schemes (PKI, MACs), trust based schemes, secure access. Etc. In this paper we discuss the common threats and cyber-attacks performed by hackers around the world such as jamming attacks, network traffic attack, Sybil attacks, False data injection, location tracking. The paper also includes few possible privacy solutions i.e., strong privacy enhancing technologies and efficient data encryption techniques that could prevent the breach of personal identifiable information (PII). The use of few deep learning techniques to improve the precision of intrusion detection in V2X such as Convolution neural networks (CNN), Recurrent neural networks (RNN), Graph Neural networks (GNN) are also surveyed with the authors opinions expressed contextually. Keywords: V2X communication, Vehicular networks, connectivity, interference, interoperability, scalability, reliability, stability, security, and privacy.


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