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基于BO-LightGBM方法的GNSS列车定位欺骗攻击识别

Spoofing attack recognition for GNSS-based train positioning using a BO-LightGBM method.

作者信息

Bi Jiaqi, Liu Jiang, Cai Baigen, Wang Jian

机构信息

School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China.

Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing, China.

出版信息

Sci Prog. 2024 Oct-Dec;107(4):368504241272731. doi: 10.1177/00368504241272731.

DOI:10.1177/00368504241272731
PMID:39351631
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11450585/
Abstract

Trustworthy positioning is critical in the operational control and management of trains. For a train positioning system (TPS) based on a global navigation satellite system (GNSS), a spoofing attack significantly threatens the trustworthiness of positioning. However, the influence and recognition of GNSS spoofing attacks are not considered in the existing research on GNSS-enabled TPS. Spoofing attacks affect the performance of GNSS observations and the positioning results, allowing the development of data-driven spoofing recognition solutions. This study aims to achieve effective spoofing recognition for active security protection in TPS. Different features were designed to reflect the effects of a spoofing attack, including GNSS observation-related indicators and odometer-enabled parameters, and a novel Bayesian optimization-light gradient boosting machine (BO-LightGBM) solution was proposed. In particular, a Bayesian optimization technique was introduced into the LightGBM framework to improve the hyperparameter determination capability for recognition model training. Using a GNSS spoofing test platform with a specific GNSS signal generator and the SimSAFE spoofing test tool, different spoofing attack modes were tested to collect sample datasets for model training and evaluation. The results of model establishment and comparison of the model performance indicators illustrated the advantages of the proposed solution, its adaptability to different spoofing attack situations, and its superiority over state-of-the-art modeling strategies.

摘要

可靠定位在列车运行控制与管理中至关重要。对于基于全球导航卫星系统（GNSS）的列车定位系统（TPS），欺骗攻击严重威胁定位的可靠性。然而，现有关于基于GNSS的TPS的研究未考虑GNSS欺骗攻击的影响和识别。欺骗攻击会影响GNSS观测性能和定位结果，从而促使数据驱动的欺骗识别解决方案的发展。本研究旨在实现对TPS主动安全保护的有效欺骗识别。设计了不同特征以反映欺骗攻击的影响，包括与GNSS观测相关的指标和启用里程计的参数，并提出了一种新颖的贝叶斯优化-轻梯度提升机（BO-LightGBM）解决方案。特别是，将贝叶斯优化技术引入LightGBM框架，以提高识别模型训练的超参数确定能力。使用具有特定GNSS信号发生器的GNSS欺骗测试平台和SimSAFE欺骗测试工具，测试了不同的欺骗攻击模式，以收集用于模型训练和评估的样本数据集。模型建立结果和模型性能指标比较说明了所提解决方案的优势、其对不同欺骗攻击情况的适应性以及相对于现有先进建模策略的优越性。

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