| dc.contributor | 資訊系 | |
| dc.creator (作者) | 孫士勝 | |
| dc.creator (作者) | Fan, Chia-Hao;Chiang, Tsung-Wei;Sun, Shi-Sheng;Chiang, Yi-Han | |
| dc.date (日期) | 2025-12 | |
| dc.date.accessioned | 7-May-2026 16:22:36 (UTC+8) | - |
| dc.date.available | 7-May-2026 16:22:36 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-May-2026 16:22:36 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=182386 | - |
| dc.description.abstract (摘要) | The Internet of Vehicles (IoV) enables vehicles to communicate with each other and infrastructure through basic safety messages (BSMs), thereby reducing accidents by means of early warnings and collision avoidance. In fact, IoV networks are vulnerable to positional attacks induced by malicious vehicles, which can severely compromise traffic safety and system reliability. In spite of the existing works devoted to misbehavior detection for certain types of attacks, extracting statistical features from BSMs to defend against all positional attacks in IoV networks has been largely overlooked. In this paper, we extract six types of positional attacks from the VeReMi-Extension dataset and discover hidden attack patterns. Based on these discovered patterns and our data analysis, we categorize them into the statistical features of Gaussian-like, Lévy-like sparse, and Lévy-like ordinary positional attacks. Then, we propose a three-feature misbehavior detection mechanism to distinguish between normal and malicious vehicles, where the first feature detection employs the Jarque-Bera test to identify Gaussian-like positional attacks, the second feature detection identifies Lévy-like sparse positional attacks by evaluating data sparsity, and the third feature detection uses Isolation Forest to detect Lévy-like ordinary positional attacks. Simulation results show that the proposed solution achieves promising false alarm and miss detection rates, and acts robustly across diverse parameter settings. | |
| dc.format.extent | 115 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 2025 IEEE Global Communications Conference (GLOBECOM) Proceedings, IEEE Communications Society, pp.5937-5942 | |
| dc.subject (關鍵詞) | Internet of Vehicles; positional attacks; misbehavior detection; VeReMi-Extension dataset | |
| dc.title (題名) | Statistical Feature-Based Misbehavior Detection Against Positional Attacks in Internet of Vehicles | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/GLOBECOM59602.2025.11432779 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/GLOBECOM59602.2025.11432779 | |
