16737 latest Fairness/Ethics + ML/AI papersA neural-network based anomaly detection system and a safety protocol to protect vehicular network
arXiv:2411.07013v1 »Full PDF »Master's thesis 2023-2024
This thesis addresses the use of Cooperative Intelligent Transport Systems
(CITS) to improve road safety and efficiency by enabling vehicle-to-vehicle
communication, highlighting the importance of secure and accurate data
exchange. To ensure safety, the thesis proposes a Machine Learning-based
Misbehavior Detection System (MDS) using Long Short-Term Memory (LSTM) networks
to detect and mitigate incorrect or misleading messages within vehicular
networks. Trained offline on the VeReMi dataset, the detection model is tested
in real-time within a platooning scenario, demonstrating that it can prevent
nearly all accidents caused by misbehavior by triggering a defense protocol
that dissolves the platoon if anomalies are detected. The results show that
while the system can accurately detect general misbehavior, it struggles to
label specific types due to varying traffic conditions, implying the difficulty
of creating a universally adaptive protocol. However, the thesis suggests that
with more data and further refinement, this MDS could be implemented in
real-world CITS, enhancing driving safety by mitigating risks from misbehavior
in cooperative driving networks.