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…

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 proposes a proactive, resilient architecture for autonomous vehicles by integrating redundancy, diversity, and adaptive reconfiguration to defend against various cyber and physical attacks.

This paper characterizes the impact of ETSI Decentralized Congestion Control (DCC) on age of information (AoI) for vehicle-to-infrastructure updates, revealing a hyperbolic density dependence and prop…

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.

This paper proposes a decentralized Multi-Agent Reinforcement Learning framework with a Graph Neural Network for connected and autonomous vehicles to reduce traffic shockwaves.

This paper introduces Path Consistency Scoring (PCS), a framework that evaluates router geolocation as a path-level consistency problem and produces a path consistency score based on a Hidden Markov M…

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 introduces CCLab, an adversarial testing framework, to systematically evaluate the robustness of both learning-based and traditional congestion controllers, finding that learning-based contr…

The paper proposes a trust-aware federated hybrid intrusion detection framework using multiple ML models at distributed edge nodes to proactively secure highly connected Intelligent Transport Systems.

This paper presents a dynamic multi-layer equilibrium attractor for disrupted urban mobility, establishes conditions for exact and approximate projectability of the attractor, and shows that two obser…

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…

The SAFE approach enhances fault-tolerant trust management in VANETs by ensuring vehicles send updated feedback reports before leaving a witness area, significantly reducing erroneous penalization of…

PAMPOS introduces a causal transformer-decoder that learns normal mobility patterns from benign V2X trajectories, enabling attack-agnostic misbehavior detection by identifying deviations from predicte…

This paper introduces a correction-aware network model to study the spread of erroneous claims in communication networks and characterizes the intersection of reliability and error control constraints…

The paper introduces a genetic algorithm framework to calibrate complex urban traffic simulations using only sparse real-world traffic observations, eliminating the need for detailed employment data.

This paper demonstrates that a specific routing-layer defense mechanism in OLSR-based MANETs can be inferred from passively observable routing and control-plane behavior, even when the defense operate…