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

IF 2.6 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Science Progress Pub Date : 2024-10-01 DOI:10.1177/00368504241272731
Jiaqi Bi, Jiang Liu, Baigen Cai, Jian Wang
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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.

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使用 BO-LightGBM 方法识别基于 GNSS 的列车定位的欺骗攻击。
可靠的定位对于列车的运行控制和管理至关重要。对于基于全球导航卫星系统(GNSS)的列车定位系统(TPS)来说,欺骗攻击会严重威胁定位的可信度。然而,现有的有关全球导航卫星系统列车定位系统的研究并未考虑全球导航卫星系统欺骗攻击的影响和识别问题。欺骗攻击会影响 GNSS 观测性能和定位结果,因此需要开发数据驱动的欺骗识别解决方案。本研究旨在实现有效的欺骗识别,为 TPS 提供主动安全保护。设计了不同的特征来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表的参数,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。特别是在 LightGBM 框架中引入了贝叶斯优化技术,以提高识别模型训练的超参数确定能力。利用带有特定 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,收集了用于模型训练和评估的样本数据集。模型建立的结果和模型性能指标的比较说明了所提出解决方案的优势、对不同欺骗攻击情况的适应性以及与最先进建模策略相比的优越性。
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来源期刊
Science Progress
Science Progress Multidisciplinary-Multidisciplinary
CiteScore
3.80
自引率
0.00%
发文量
119
期刊介绍: Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.
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