Cybersecurity-Focused Anomaly Detection in Connected Autonomous Vehicles Using Machine Learning

TL;DR

This paper presents a machine learning approach using stacked LSTM and Random Forest models to detect anomalies in connected autonomous vehicles, enhancing safety and reliability in transportation networks.

Contribution

It introduces a novel dataset and applies deep learning and ensemble methods for effective anomaly detection in CAVs, improving detection accuracy and interpretability.

Findings

Stacked LSTM achieved an R2 of 0.9998 and MAE of 82.425.

Random Forest achieved an R2 of 0.9830 and MAE of 5.746.

Models effectively detect anomalies in vehicle behavior.

Abstract

Anomaly detection in connected autonomous vehicles (CAVs) is crucial for maintaining safe and reliable transportation networks, as CAVs can be susceptible to sensor malfunctions, cyber-attacks, and unexpected environmental disruptions. This study explores an anomaly detection approach by simulating vehicle behavior, generating a dataset that represents typical and atypical vehicular interactions. The dataset includes time-series data of position, speed, and acceleration for multiple connected autonomous vehicles. We utilized machine learning models to effectively identify abnormal driving patterns. First, we applied a stacked Long Short-Term Memory (LSTM) model to capture temporal dependencies and sequence-based anomalies. The stacked LSTM model processed the sequential data to learn standard driving behaviors. Additionally, we deployed a Random Forest model to support anomaly detection by offering ensemble-based predictions, which enhanced model interpretability and performance. The Random Forest model achieved an R2 of 0.9830, MAE of 5.746, and a 95th percentile anomaly threshold of 14.18, while the stacked LSTM model attained an R2 of 0.9998, MAE of 82.425, and a 95th percentile anomaly threshold of 265.63. These results demonstrate the models' effectiveness in accurately predicting vehicle trajectories and detecting anomalies in autonomous driving scenarios.