A neural-network based anomaly detection system and a safety protocol to protect vehicular network

TL;DR

This paper presents a machine learning-based anomaly detection system using LSTM networks to identify misbehavior in vehicular networks, significantly improving safety in cooperative driving scenarios.

Contribution

It introduces a novel LSTM-based misbehavior detection system trained on real datasets, capable of real-time anomaly detection to enhance vehicular network security.

Findings

High accuracy in detecting general misbehavior

Effective prevention of accidents caused by misbehavior

Challenges in classifying specific misbehavior types

Abstract

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.