| Title | Artificial Intelligence-Based Cybersecurity for Autonomous Vehicles: A Multi-Layer Framework for Threat Detection and Resilient Decision-Making |
| Research Area | Autonomous Vehicles |
| Abstract | Autonomous vehicles (AVs) represent a major transformation in modern transportation by combin-ing sensing, communication, artificial intelligence, embedded computing, navigation, and automated control. These vehicles continuously collect information from cameras, LiDAR, radar, inertial sen-sors, positioning systems, vehicle networks, and external communication infrastructure. Artificial intelligence enables autonomous vehicles to interpret their environment, predict the behavior of sur-rounding objects, determine driving strategies, and execute vehicle-control decisions. However, the same dependence on software, connectivity, sensors, and AI introduces a broad cybersecurity attack surface. Recent research has identified threats involving sensor manipulation, spoofing, jamming, network intrusion, data injection, adversarial machine learning, and vulnerabilities in connected ve-hicle infrastructure. (Springer)
This paper presents a comprehensive examination of artificial intelligence-based cybersecurity for autonomous vehicles and proposes a conceptual multi-layer framework for threat detection and resil-ient decision-making. The framework integrates sensor-level monitoring, in-vehicle network analy-sis, vehicle-to-everything communication monitoring, AI-based anomaly detection, risk assessment, secure decision-making, and recovery mechanisms. Unlike approaches that focus exclusively on classification accuracy, the proposed framework considers detection latency, false-positive rates, model robustness, computational requirements, explainability, and operational consequences. |
| Keywords | Autonomous Vehicles, Artificial Intelligence, Cybersecurity, Machine Learning, Intru-sion Detection, CAN Bus, Sensor Security, Anomaly Detection, Adversarial Attacks, Vehicle-to-Everything Communication, Explainable AI |
| Paper Status | Published |
| Volume | 2 |
| Issue | 3 |
| Published On | 22/05/2026 |
| Published File |
IJSRTD_4881.pdf
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