Temporal transformer CAN encoder with federated lightweight heads for anomaly detection

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Computer Science > Machine Learning arXiv:2610.10613 (cs) [Submitted on 7 Oct 2026] Title: Temporal transformer CAN encoder with federated lightweight heads for anomaly detection Authors: Konstantinos Gyftodimos, Kyriakos Chiotis, Elena Politi, George Dimitrakopoulos, Eirini Liotou.

计算机科学 > 机器学习 arXiv:2610.10613 (cs) [提交于 2026 年 10 月 7 日] 标题：用于异常检测的联邦轻量级头部时间 Transformer CAN 编码器作者：Konstantinos Gyftodimos, Kyriakos Chiotis, Elena Politi, George Dimitrakopoulos, Eirini Liotou。

Abstract: 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.

摘要：现代车辆依赖于大量的电子控制单元（ECU），这些单元通过控制器局域网（CAN）总线不断交换信息。由于这种通信的快速性、结构性和重复性，即使是时间、有效载荷值或消息模式上的细微变化也可能指向异常活动。无论是由于错误、故障还是蓄意干扰，这些异常通常都很微妙，且难以通过分别处理消息或依赖手动创建规则的传统方法来识别。

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.

受此差距的启发，我们提出了一种用于车载网络异常检测的隐私保护框架，该框架基于带有联邦轻量级头部的时间 Transformer CAN 编码器，以更好地捕捉这些不规则现象。轻量级 Transformer 编码器通过学习这些信号随时间的演变，实现了对微妙的时间和上下文异常的检测，而联邦学习机制使多辆车或多个 ECU 能够协同工作以改进共享模型，且无需交换原始 CAN 数据。

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.

根据在开源数据集上进行的实验，这种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。