发表机构
ANADELTA P.C.; Harokopio University of Athens（ANADEL塔公司; 阿萨索皮奥雅典大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种带联邦轻量头的时间Transformer CAN编码器框架，用于车载网络异常检测，结合联邦学习与时间序列建模，在保障隐私和效率的同时实现稳健检测。
AI 中文摘要
现代车辆依赖大量电子控制单元（ECU），这些单元通过控制器局域网（CAN）总线持续交换信息。由于这种通信的快速性、结构和重复性，即使是时序、有效载荷值或消息模式的微小变化也可能指向异常活动。无论是因错误、故障还是故意干扰导致，这些异常通常很微妙，难以用单独处理消息或依赖手动创建规则的传统方法识别。针对这一差距，我们提出一种车载网络异常检测的隐私保护框架，基于带联邦轻量头的时间Transformer CAN编码器，以更好捕捉这些不规则性。轻量Transformer编码器学习这些信号随时间的演变，实现对微妙时序和上下文异常的检测；联邦学习机制使多辆车辆或ECU能协同优化共享模型，无需交换原始CAN数据。在开源数据集上开展的实验表明，联邦学习与时间序列建模的结合在保持效率和隐私的同时，提供了稳健的异常检测性能。
英文摘要
Modern vehicles rely on large numbers of Electronic Control Units (ECUs) that constantly exchange information over the Controller Area Network (CAN) bus. Due to the rapidity, structure, and repetition of this communication, even slight variations in timing, payload values, or message patterns can point to unusual activity. Whether due to errors, malfunctions, or deliberate interference, these anomalies are frequently subtle and challenging to identify with conventional methods that handle messages separately or rely on manually created rules. Motivated by this gap, we present a privacy-preserving framework for anomaly detection in in-vehicle networks, based on a Temporal Transformer CAN Encoder with Federated Lightweight Heads, to better capture these irregularities. The detection of subtle temporal and contextual anomalies is made possible by a lightweight Transformer encoder that learns how these signals evolve over time, while a federated learning mechanism enables several vehicles or ECUs to work together to improve a shared model without exchanging raw CAN data. This combination of federated learning and temporal sequence modeling provides robust anomaly detection performance while maintaining efficiency and privacy, according to experiments conducted on open-source datasets.
Journal refPresented at ITS European Congress 2025
