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.

翻译：入侵检测系统（IDS）被广泛用于检测和缓解外部网络安全事件。随着互联自动驾驶汽车（CAVs）的发展，车载自组网（VANETs）正不断演进。因此，评估传统IDS方法如何应用于新兴技术至关重要。针对这一问题，本研究提出了一种基于堆叠集成学习的IDS方法，该方法结合多种机器学习算法，比单一算法方法更有效地检测威胁。利用CICIDS2017和VeReMi基准数据集，我们将所提方法与现有机器学习方法进行性能比较，发现其在威胁识别方面准确性更高。此外，该方法还通过超参数优化和特征选择进一步提升了性能。总体结果表明，堆叠集成学习是增强IDS有效性的一种有前景的技术。