Securing automotive data flow: A survey of telematics security across intra-vehicle, V2X, and cloud layers

This survey systematically analyzes 77 studies on automotive telematics security, examining solutions across Intra-Vehicle Networks, Vehicle-to-Everything (V2X) communications, and Cloud/IoT (Internet of Things) integration. Through comparison with 13 prior surveys, we identify a critical research gap: existing literature predominantly addresses these layers in isolation, failing to adequately secure data flows across architectural boundaries. Our cross-layer analysis reveals that hybrid intrusion detection systems achieve up to 99.5% accuracy against CAN bus attacks, federated learning frameworks attain 99% DDoS detection while preserving privacy, and secure interface mechanisms enable sub-100 μ s latency transitions with 52% reduced overhead for over-the-air updates. We closely examine intrusion detection systems, secure communication protocols, privacy protection techniques, and attack resilience strategies, identifying both the strengths and limitations of current approaches. Additionally, we evaluate how emerging technologies such as artificial intelligence and machine learning can improve the resilience of telematics systems against advanced cyber threats. Our findings indicate that securing automotive data requires a unified, cross-layer cybersecurity strategy to ensure data integrity, confidentiality, and availability, guiding future research in this vital area.