Collaborative Approaches to Enhancing Smart Vehicles Cybersecurity Through AI-Driven Threat Detection - Syed Atif Ali; Salwa Din (LPPM Universitas Bina Sarana Informatika, 2026)
This paper thoroughly investigates collaborative approaches to enhancing smart vehicles' related cybersecurity through AI-driven threat detection. As connected and automated vehicles (CAVs) become rapidly in demand, new vulnerabilities emerge alongside technological progress. We explored how integration of 5G networks, blockchain system, and quantum computing can address these security related challenges. Our study emphasizes the critical role of intrusion detection systems (IDS), AI-based pattern techniques, and interdisciplinary collaboration across academia, industry, and private sector. We present a roadmap incorporating secure hardware/software stacks and advanced threat intelligence to mitigate cybersecurity threats in autonomous vehicles. We address these challenges, by proposing a multi-layer AI-driven cybersecurity architecture by integrating in-vehicle anomaly detection, cloud-based correlation, and privacy-preserving federated learning. We validated the framework by using a hybrid simulation and edge-device testbed environment. Our results shows improved detection performance (F1-score: 0.97), as well as; enhanced adversarial robustness (89% under FGSM attack), and sub-50 ms real-time response capability while maintaining data privacy through local model training.