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Found 9 papers, 0 papers with code
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems
The recent application of Federated Learning algorithms in IOT and Wireless vehicular networks have given rise to newer cyber threats in the mobile environment which hitherto were not present in traditional fixed networks.
Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
Recent advancements in deep learning techniques have opened new possibilities for designing solutions for autonomous cyber defence.
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning
We propose a technique which consists in decoupling the monitoring phase from the detection phase in defenses against poisoning attacks in a decentralized federated learning deployment that aim at monitoring the behavior of the workers.
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference
As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode.
On the Initial Behavior Monitoring Issues in Federated Learning
In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief.
Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems
We identified a number of attack strategies conducted by the malicious CAVs to disrupt the training of the global model in vehicular networks.
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation
We propose attestedFL, a defense mechanism that monitors the training of individual nodes through state persistence in order to detect a malicious worker.
Resilience-by-design in Adaptive Multi-Agent Traffic Control Systems
Connected and Autonomous Vehicles (CAVs) with their evolving data gathering capabilities will play a significant role in road safety and efficiency applications supported by Intelligent Transport Systems (ITS), such as Traffic Signal Control (TSC) for urban traffic congestion management.
Autonomous Vehicles
Cryptography and Security
Prediction of Traffic Flow via Connected Vehicles
To cope with the fact that existing approaches do not adapt to variation in traffic, we show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of the various events that CV realistically encountered on segments along their trajectory.
