The Internet of Vehicles (IoV) may face challenging cybersecurity attacks that may require sophisticated intrusion detection systems, necessitating a rapid development and response system. This research investigates the performance advantages of GPU-accelerated libraries (cuML) compared to traditional CPU-based implementations (scikit-learn), focusing on the speed and efficiency required for machine learning models used in IoV threat detection environments. The comprehensive evaluations conducted employ four machine learning approaches (Random Forest, KNN, Logistic Regression, XGBoost) across three distinct IoV security datasets (OTIDS, GIDS, CICIoV2024). Our findings demonstrate that GPU-accelerated implementations dramatically improved computational efficiency, with training times reduced by a factor of up to 159 and prediction speeds accelerated by up to 95 times compared to traditional CPU processing, all while preserving detection accuracy. This remarkable performance breakthrough empowers researchers and security specialists to harness GPU acceleration for creating faster, more effective threat detection systems that meet the urgent real-time security demands of today's connected vehicle networks.

翻译：车联网（IoV）可能面临严峻的网络安全攻击，这需要复杂的入侵检测系统，并催生对快速开发与响应系统的需求。本研究探究了GPU加速库（cuML）相较于传统基于CPU的实现（scikit-learn）的性能优势，重点关注车联网威胁检测环境中机器学习模型所需的速度与效率。所进行的综合评估在三个不同的车联网安全数据集（OTIDS、GIDS、CICIoV2024）上采用了四种机器学习方法（Random Forest、KNN、Logistic Regression、XGBoost）。我们的研究结果表明，GPU加速实现显著提升了计算效率：与传统CPU处理相比，训练时间最多缩短了159倍，预测速度最多加快了95倍，同时保持了检测精度。这一显著的性能突破使研究人员和安全专家能够利用GPU加速来构建更快、更有效的威胁检测系统，以满足当今互联车辆网络对实时安全的迫切需求。