Enhanced Ensemble Temporal Convolutional Network With Genetic Algorithm Optimization for Anomaly Detection in CAVs

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Abstract
Malicious attacks on Connected and Automated Vehicle (CAV) systems can severely impact transportation safety and efficiency. They can manipulate sensor readings, disrupt service, or falsify environmental perceptions, leading to incorrect decisions and potential catastrophes. Detecting such anomalies is complex due to challenges such as false positives and negatives, sensor interference, resource constraints, and diverse types of anomalies. Existing traditional methods, including statistical approaches, signature-based detection, behavioral analysis, and network intrusion detection systems, often suffer from high false positive rates, sensitivity to noise, reliance on thresholds, and inability to detect evolving or subtle patterns. Recent machine learning and hybrid methods improve on traditional techniques but struggle with frequency bias and time scale selection, limiting real-time applications in CAVs networks. To address these challenges, we propose ET-DRF-GA, a novel method that combines an ensemble-based exploration of Temporal Convolutional Networks (TCN), Differential Receptive Fields (DRF), and Genetic Algorithms (GA). This approach offers dynamic parameter adjustment via genetic algorithms, enhanced robustness and stability through ensemble-based model selection, and optimized sensitivity to different anomaly types using DRF. Experiments on benchmark datasets, Safety Pilot Model Deployment (SPMD) and Vehicular Reference Misbehavior (VeReMi), demonstrate the method’s robustness and effectiveness in anomaly detection for CAV systems, paving the way for future research in this critical domain.
Funding Information
  • Research Assistant Funding Program (RAFP) at Algoma University

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