Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908298403381248 |
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| author | Çolhak, Furkan Coşkun, Hasan Cyrille, Tsafac Nkombong Regine Hoxa, Tedi Ecevit, Mert İlhan Aydın, Mehmet Nafiz |
| author_facet | Çolhak, Furkan Coşkun, Hasan Cyrille, Tsafac Nkombong Regine Hoxa, Tedi Ecevit, Mert İlhan Aydın, Mehmet Nafiz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries Çolhak, Furkan Coşkun, Hasan Cyrille, Tsafac Nkombong Regine Hoxa, Tedi Ecevit, Mert İlhan Aydın, Mehmet Nafiz Machine Learning Artificial Intelligence Cryptography and Security 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. |
| title | Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries |
| topic | Machine Learning Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2504.01905 |