This paper proposes a simplified Temporal Convolutional Network-based estimator to improve channel estimation in vehicular communication.

The FALCON-C framework proposes a flow-based autoencoder approach to detect cyber anomalies and label malicious flows in connected vehicular networks, achieving high accuracy in identifying attacks on…

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.

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…

This paper presents a comprehensive survey on reconfigurable antennas for next-generation mobile networks, focusing on their potential and applications.

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 addresses the lack of independent measurement tools for modern mobile communication by designing and implementing open-source platforms to study cellular radio networks, operator services, a…

This paper evaluates the security of industrial control systems (ICS) transitioning to 5G communication, finding that while optimal conditions allow for resilience, degraded channel conditions signifi…

The paper proposes a communication-centric 6G-LLM architecture for tactical autonomous defense vehicles, demonstrating significant improvements in coordination and communication efficiency over conven…

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 identifies and demonstrates eight novel attack scenarios exploiting the ISO 15765-2 transport protocol over CAN, showing that three can successfully induce denial of diagnostic services in…

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.

The paper introduces EvoXXLTraffic, an ultra-large, sensor-evolving dataset that simulates real-world road network growth, demonstrating that existing state-of-the-art traffic forecasting models fail…

The paper introduces CAN-QA, a novel question-answering benchmark that reformulates CAN traffic analysis from a classification task to a reasoning task, demonstrating that current LLMs struggle with c…

This paper analyzes MQTT security in IoT, demonstrating critical vulnerabilities like eavesdropping and DoS due to weak encryption and authentication, and proposes mitigation strategies.

The paper demonstrates that using lightweight block cipher encryption on CAN payloads can effectively prevent passive reverse engineering of signal semantics without significantly impacting real-time…

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 proposes Multi-Order Communication (MOC) to overcome the limitations of standard first-order message passing in LLM-based multi-agent systems, significantly improving performance by capturin…

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.

The paper proposes a scalable, market-analysis-driven methodology to assess national charging station cybersecurity by extrapolating field test results from a manageable subset of stations to estimate…