This paper systematically analyzes 48 studies on perception attacks against autonomous vehicles, revealing that the increasing reliance on multi-sensor fusion creates new, complex vulnerabilities that…

The paper introduces TrustFlip, a novel physical adversarial attack that exploits consistency-based trust defenses in vehicular collaborative perception by using genuine objects to induce inconsistenc…

The paper introduces a stealthy, scenario-realistic data fabrication attack that subtly manipulates object poses in shared perception data to induce unsafe driving behaviors in connected and autonomou…

This survey provides a comprehensive, systematic roadmap for achieving cyber-physical system (CPS) resilience by integrating five interconnected themes: system-wide properties, handling data scarcity…

The paper demonstrates a coordinated, cross-modal spoofing attack that successfully deceives state-of-the-art multi-sensor fusion systems in autonomous vehicles by making multiple sensors agree on a f…

The paper introduces a novel adversarial attack that uses static, view-dependent camouflage on a vehicle to induce consistent feature drift, causing autonomous systems to predict false, yet plausible,…

This paper reviews the current state of cybersecurity for EV charging infrastructure, analyzing existing machine learning countermeasures and proposing future directions to overcome data limitations i…

The paper introduces a novel pipeline integrating formal verification and process mining to systematically identify and analyze root causes of security property invalidations in complex automotive net…

This paper surveys how integrating Edge Computing, Machine Learning, and Deep Learning can enhance the security and resilience of complex Internet of Vehicles (IoV) networks.

This paper demonstrates that reasoning-enabled Vision-Language-Action (VLA) models for autonomous driving are highly vulnerable to realistic input perturbations, significantly compromising both reason…

The paper introduces a threat-oriented digital twinning methodology to enable reproducible and controllable cybersecurity evaluation of autonomous platforms, overcoming limitations in accessing real-w…

This paper provides a comprehensive review of network optimization aspects for Connected and Autonomous Vehicles (CAVs), aiming to clarify misconceptions and outline future research directions.

FedTrident proposes a comprehensive framework to defend Federated Learning-based Road Condition Classification against Targeted Label-Flipping Attacks, achieving robust performance comparable to non-a…

The paper proposes IPEK, a context-aware trust mechanism for VANETs, which significantly improves detection of intelligent attackers by incorporating event and location severity into trust calculation…

This paper demonstrates that Unmanned Aerial Vehicle (UAV) autopilot fail-safe mechanisms are vulnerable to non-invasive voltage glitch fault injection, potentially allowing attackers to suppress crit…

The paper proposes an autonomous red teaming framework combining LLMs and RL to generate sophisticated, multi-stage cyber attack campaigns, demonstrating its necessity for evaluating robust AI-enabled…

The paper proposes CANGuard, a hybrid CNN-GRU-Attention deep learning model, to accurately detect sophisticated Denial-of-Service and spoofing attacks targeting critical in-vehicle CAN bus networks.

This paper introduces a foundational framework and taxonomy for managing catastrophic AI loss of control (LOC) incidents, providing a proportional guide for response based on the severity and recovera…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by GenAI, moving beyond traditional react…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by Generative AI, moving beyond tradition…