The field of autonomous vehicle research is witnessing significant advancements in security and resilience. Recent studies have focused on developing innovative frameworks and techniques to detect and prevent various types of attacks, including DDoS attacks, wormhole attacks, and falsification attacks. The integration of artificial intelligence, machine learning, and blockchain technology is becoming increasingly prominent in these efforts. Noteworthy papers in this area include: A Data-Driven Probabilistic Framework for Cascading Urban Risk Analysis, which presents a foundational Bayesian network-based approach for analyzing cross-domain risk propagation. AttentionGuard, a transformer-based framework for misbehavior detection in vehicle platooning systems, achieves high detection accuracy and robust performance during complex maneuvers.
Advancements in Autonomous Vehicle Security and Resilience
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A Data-Driven Probabilistic Framework for Cascading Urban Risk Analysis Using Bayesian Networks
AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles
Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey
Machine Learning-Based Detection of DDoS Attacks in VANETs for Emergency Vehicle Communication
Modeling Interdependent Cybersecurity Threats Using Bayesian Networks: A Case Study on In-Vehicle Infotainment Systems
CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations
Wormhole Detection Based on Z-Score And Neighbor Table Comparison
DeFeed: Secure Decentralized Cross-Contract Data Feed in Web 3.0 for Connected Autonomous Vehicles
AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons