SPADE：从网联车视角进行SPaT攻击检测
SPADE: SPaT Attack Detection from the Connected Vehicle's Perspective
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中文总结 AI 辅助
针对现有研究忽略网联车视角SPaT攻击检测的问题，推出带标注多模态仿真数据集SPADE，助力C-V2X安全的深度学习入侵检测研究。
中文摘要 AI 辅助
信号相位与时序（SPaT）消息是网联车（CV）安全的基石，使CV能通过车路协同（V2I）和车车协同（V2V）通信感知并响应交叉口状态。这些消息的完整性面临一系列应用层攻击的威胁，当路侧单元或对等车辆被攻陷时，这类攻击可绕过常规认证。现有入侵检测研究要么防御基础设施侧，要么针对V2V基本安全消息（BSM）/协同感知消息（CAM）的异常行为，却忽略了车载CV视角下的SPaT完整性问题。为填补这一空白，我们推出SPADE——即SPaT攻击检测与评估数据集，这是一个带标注的多模态仿真数据集，专为该领域的深度学习入侵检测系统（IDS）研究设计。SPADE通过Eclipse MOSAIC生成，在SAE J2735应用层注入运行时攻击，涵盖6类攻击和1类良性类别。结合4种交叉口几何构型、6种运行工况及5次独立随机种子重复，SPADE包含180个独特的基础场景运行，产生约1890000条带标注的时间步记录（每类270000条）。每条记录融合SPaT消息字段、车载摄像头置信度得分及协同V2V对等数据，共40个特征，反映区分蓄意攻击与环境退化所需的多模态信号空间。该数据集、生成代码及场景配置已公开，以支持C-V2X安全领域可复现且具可比性的IDS研究。开发的工具箱、说明及数据集链接已在GitHub公开：[此处为链接]。
英文摘要
Signal Phase and Timing (SPaT) messages are a cornerstone of connected vehicle (CV) safety, enabling CVs to perceive and respond to intersection state through Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication. The integrity of these messages is threatened by a range of application-layer attacks that can bypass conventional authentication when a roadside unit or peer vehicle is compromised. Existing intrusion detection research either defends the infrastructure side or targets V2V Basic Safety Message (BSM) / Cooperative Awareness Message (CAM) misbehavior, leaving the onboard CV perspective on SPaT integrity unaddressed. To close this gap, we introduce SPADE --- the SPaT Attack Detection and Evaluation dataset --- a labelled, multi-modal, simulation-based dataset designed specifically for deep learning IDS research in this space. SPADE is generated through Eclipse MOSAIC using runtime attack injection at the SAE J2735 application layer across six attack classes and one benign class. By combining four intersection geometries, six operating conditions, and five independent random-seed repetitions, SPADE comprises 180 unique base scenario runs, yielding $\sim$1,890,000 labelled timestep records (270,000 per class). Each record fuses SPaT message fields, onboard camera confidence scores, and cooperative V2V peer data across 40 features, reflecting the multi-modal signal space required to distinguish deliberate attacks from environmental degradation. The dataset, generation code, and scenario configurations are released publicly to support reproducible and comparative IDS research in C-V2X security. The developed toolbox, instructions, and dataset link are publicly available on GitHub: https://github.com/jdinovo/SPADE.
发表机构
- Royal Military College of Canada（加拿大陆军军事学院）
- Department of Electrical and Computer Engineering, Faculty of Engineering, Royal Military College of Canada（加拿大陆军军事学院工程学院电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。
