Intelligent Ensemble Learning Framework for Intrusion Detection in Consumer Connected and Autonomous Vehicles

Abstract

The rapid advancement of consumer connected and autonomous vehicle (CAV) technologies offers significant improvements in transportation efficiency, safety, and user convenience. However, these benefits come with substantial cybersecurity risks, as in-vehicle networks and cloud connectivity expose CAVs to increasingly sophisticated cyberattacks. Conventional intrusion detection systems (IDS) often fall short in this domain, as they are not adaptive and struggle to handle the dynamic and stealthy nature of modern attacks. To address these limitations, we propose a novel IDS framework based on a stacking ensemble architecture that integrates multiple machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), as base learners. A Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) serves as the meta-learner to capture temporal dependencies and sequential patterns in network traffic. To enhance the model’s generalization capability, we incorporate a model-agnostic meta-learning (MAML) approach into the LSTM-RNN meta-learner. The MAML-enhanced set of capabilities enables more effective detection of evolving and previously unseen attack scenarios. Simulation results demonstrate that the proposed framework consistently outperforms standalone LSTM-RNN models, traditional ensemble methods, and individual base learners in detecting complex cyberattack patterns in consumer CAV environments. These findings highlight the potential of meta-learning-driven ensemble IDS frameworks for securing next-generation intelligent transportation systems.