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<p>The road safety and traffic efficiency is enhanced by providing communication between Vehicles in Vehicle Ad hoc networks (VANETs). Position falsification attacks represent a significant threat in VANETs, where the accuracy and integrity of location-based information are critical for safe and efficient transportation. The misbehavior detection frameworks can effectively identify position falsification attacks. The frameworks employ machine learning classifiers trained on features derived from inter-vehicular communication data. Optimization can enhance a stacked ensemble model for misbehaviour detection by hyperparameter tuning of classifiers. The proposed methodology involves constructing a stacked ensemble model composed of five diverse base classifiers, such as K Nearest Neighbours Classifier (KNN), Ada Boost Classifier (ADA), Extra Trees Classifier (ETC), Random Forest (RF), and Extreme Gradient Boosting Classifier (XGBC). Meta Classifier is used to combine predictions from the individual classifiers with logistic regression. To achieve the highest accuracy, Artificial Bee Colony (ABC) optimization is used to enhance the hyperparameters for the base classifiers. The optimized stacked ensemble model shows that our model provides the best results when compared with the existing methods.</p>

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Optimized stacked ensemble approach for detecting position falsification in VANETs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • K. Saranya,
  • S. Ramakrishnan

摘要

The road safety and traffic efficiency is enhanced by providing communication between Vehicles in Vehicle Ad hoc networks (VANETs). Position falsification attacks represent a significant threat in VANETs, where the accuracy and integrity of location-based information are critical for safe and efficient transportation. The misbehavior detection frameworks can effectively identify position falsification attacks. The frameworks employ machine learning classifiers trained on features derived from inter-vehicular communication data. Optimization can enhance a stacked ensemble model for misbehaviour detection by hyperparameter tuning of classifiers. The proposed methodology involves constructing a stacked ensemble model composed of five diverse base classifiers, such as K Nearest Neighbours Classifier (KNN), Ada Boost Classifier (ADA), Extra Trees Classifier (ETC), Random Forest (RF), and Extreme Gradient Boosting Classifier (XGBC). Meta Classifier is used to combine predictions from the individual classifiers with logistic regression. To achieve the highest accuracy, Artificial Bee Colony (ABC) optimization is used to enhance the hyperparameters for the base classifiers. The optimized stacked ensemble model shows that our model provides the best results when compared with the existing methods.