Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection

• URL: http://arxiv.org/abs/2501.00261v1
• Date: Tue, 31 Dec 2024 04:08:42 GMT
• Title: Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection
• Authors: Syed Atif Ali, Salwa Din,
• Abstract summary: The automotive industry increasingly adopts connected and automated vehicles (CAVs) With the emergence of new vulnerabilities and security requirements, the integration of advanced technologies presents promising avenues for enhancing CAV cybersecurity. The roadmap for cybersecurity in autonomous vehicles emphasizes the importance of efficient intrusion detection systems and AI-based techniques.
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• Abstract: The introduction sets the stage for exploring collaborative approaches to bolstering smart vehicle cybersecurity through AI-driven threat detection. As the automotive industry increasingly adopts connected and automated vehicles (CAVs), the need for robust cybersecurity measures becomes paramount. With the emergence of new vulnerabilities and security requirements, the integration of advanced technologies such as 5G networks, blockchain, and quantum computing presents promising avenues for enhancing CAV cybersecurity . Additionally, the roadmap for cybersecurity in autonomous vehicles emphasizes the importance of efficient intrusion detection systems and AI-based techniques, along with the integration of secure hardware, software stacks, and advanced threat intelligence to address cybersecurity challenges in future autonomous vehicles.

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