用GPU加速提升车联网入侵检测速度，最快快95倍。
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries
- 对比了GPU加速库cuML与传统CPU库scikit-learn的性能差异。
- 训练速度最高提升159倍，预测速度最高提升95倍，准确率不变。
- 适合需要实时响应的车联网安全研究人员和工程师参考。
车联网可能面临复杂的网络安全攻击，需依赖高效的入侵检测系统，要求快速开发与响应。本研究对比了GPU加速库cuML与传统CPU实现（scikit-learn）在机器学习模型中的性能表现，聚焦于车联网威胁检测环境下的速度与效率。评估采用四种机器学习方法（随机森林、KNN、逻辑回归、XGBoost），覆盖三个车联网安全数据集（OTIDS、GIDS、CICIoV2024）。结果表明，GPU加速版本显著提升了计算效率：训练时间最高缩短159倍，预测速度最高提升95倍，同时保持原有检测准确率。这一性能突破为研究人员和安全专家提供了高效构建实时威胁检测系统的新路径。
原文摘要 · Abstract (English)
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.
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