  Intrusion Detection Systems (IDS) are widely employed to detect and mitigate
external network security events. VANETs (Vehicle ad-hoc Networks) are
evolving, especially with the development of Connected Autonomous Vehicles
(CAVs). So, it is crucial to assess how traditional IDS approaches can be
utilised for emerging technologies. To address this concern, our work presents
a stacked ensemble learning approach for IDS, which combines multiple machine
learning algorithms to detect threats more effectively than single algorithm
methods. Using the CICIDS2017 and the VeReMi benchmark data sets, we compare
the performance of our approach with existing machine learning methods and find
that it is more accurate at identifying threats. Our method also incorporates
hyperparameter optimization and feature selection to improve its performance
further. Overall, our results suggest that stacked ensemble learning is a
promising technique for enhancing the effectiveness of IDS.
