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面向云端拜占庭故障的列控应用安全计算架构OA

Secure Computing Architecture for Cloud-Based Train Control Applications Under Byzantine Faults

中文摘要英文摘要

云原生列控应用在开放计算环境中面临逻辑劫持、时序攻击和拜占庭失效等安全挑战,为此,提出一种逻辑-时序双维度可信验证架构(Logic-Temporal Dual-dimension Verification,LTDV).该架构通过Paillier同态加密构建逻辑证据链以验证程序控制流的完整性,利用时序哈希链绑定时间戳以确保行为的实时性,并基于结果驱动的过滤式多数表决机制实现轻量级的拜占庭容错.在云化列控仿真平台的临时限速场景实验中,结果表明该架构能够有效检测多种典型攻击场景,平均检测率达97.8%,在正常工况测试中,系统误报率低于0.1%.与此同时,系统时延稳定在百毫秒级别,确保开销可控.总体而言,该架构在毫秒级响应时间内显著提升云端列控计算的可信性,具有良好的工程应用前景.

Cloud-native train control applications face security challenges such as logic hijacking,timing attacks,and Byzantine failures in open computing environments.To address these issues,this paper proposes a Logic-Temporal Dual-Dimension Verification(LTDV)architecture.The proposed architecture leverages Paillier homomorphic encryption to construct a logic evidence chain for verifying the integrity of program control flow,employs a temporal hash chain to bind timestamps for ensuring real-time behavior,and implements a result-driven filtering-based majority voting mechanism to achieve lightweight Byzantine fault tolerance.Experiments conducted on a cloud-based train control simulation platform for temporary speed restriction scenarios demonstrate that the proposed architecture effectively detects various types of typical attack scenarios,achieving an average detection rate of 97.8%and a false positive rate of less than 0.1%under normal operating conditions.Meanwhile,the system latency remains stable at the hundreds of milliseconds level,ensuring controlled overhead.Overall,the proposed architecture significantly enhances the trustworthiness of cloud-based train control computing within the constraints of millisecond response times,offering promising prospects for practical engineering deployment.

芦泽和;朱力;梁雅楠

北京交通大学,北京 100044北京交通大学,北京 100044北京交通大学,北京 100044

信息技术与安全科学

云端列控临时限速逻辑验证时序哈希链同态加密拜占庭容错

cloud-based train controltemporary speed restrictionlogic verificationtemporal hash chainhomomorphic encryptionByzantine fault tolerance

《铁路通信信号工程技术》 2026 (2)

10-16,7

国家重点研发计划项目(2024YFB3108600)北京市自然科学基金重点项目(L251011)

10.3969/j.issn.1673-4440.2026.02.002

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