Contextual Study of Security and Privacy in V2X Communication for Architecture & Networking Products

<div class="section abstract"><div class="htmlview paragraph">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 have 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 key exchange or secure crypto materials and have identified the appropriate security control to face this challenge 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 private data (PII). In various scientific domains, artificial intelligence (AI) has been frequently used to enhance traditional data-driven methodologies. AI-based on the Vehicle-to-Everything (V2X) system collects data from vehicles and uses it to improve driver awareness, predict accidents and intrusions. 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. ISO (International Organization for Standardization) and IEC (International Electrotechnical Commission) have established a number of standards for vehicle-to-everything (V2X) technologies to assure interoperability, safety, and security in V2X systems. A brief detailing on these standards are also included in this paper. The paper also focuses on the application of these solution in product lines belonging to Continental Architecture and Networking (AN).</div></div>