对铁路牵引供电系统的虚假数据注入攻击建模与检测
Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems
AI总结：
本文研究铁路牵引供电系统面临的虚假数据注入攻击，提出一种全局攻击检测系统，通过分析列车电压、电流和位置传感器数据，有效检测攻击并降低误报率。
摘要：
现代城市铁路广泛使用计算机化传感和控制技术以实现安全、可靠和准时的运营。然而，这些技术可能为网络攻击者提供便利，他们绕过空气间隙，旨在造成安全事件和服务中断。本文研究针对铁路牵引供电系统（TPS）的虚假数据注入（FDI）攻击。具体而言，我们分析了两种类型的FDI攻击，即效率攻击和安全攻击，分别旨在最大化系统的总功率消耗和使列车的本地电压超过给定的安全关键阈值。为应对这些攻击，我们开发了一个全局攻击检测（GAD）系统，该系统串联了一个坏数据检测器和一个基于独特TPS特征设计的新型二次攻击检测器。在列车位置数据完整的情况下，我们的检测系统能够有效检测列车电压和电流测量的FDI攻击，即使攻击者完全且准确地了解TPS、攻击检测和实时系统状态。特别是，GAD系统具有自适应机制，确保在有噪声的系统测量下检测攻击的低误报率和误判率。大量基于真实列车运行剖面的仿真验证了TPS设置对FDI攻击的易受攻击性，但这些攻击可以被所提出的GAD系统有效检测，同时确保低误报率。
英文摘要：
Modern urban railways extensively use computerized sensing and control technologies to achieve safe, reliable, and well-timed operations. However, the use of these technologies may provide a convenient leverage to cyber-attackers who have bypassed the air gaps and aim at causing safety incidents and service disruptions. In this paper, we study false data injection (FDI) attacks against railways' traction power systems (TPSes). Specifically, we analyze two types of FDI attacks on the train-borne voltage, current, and position sensor measurements - which we call efficiency attack and safety attack -- that (i) maximize the system's total power consumption and (ii) mislead trains' local voltages to exceed given safety-critical thresholds, respectively. To counteract, we develop a global attack detection (GAD) system that serializes a bad data detector and a novel secondary attack detector designed based on unique TPS characteristics. With intact position data of trains, our detection system can effectively detect the FDI attacks on trains' voltage and current measurements even if the attacker has full and accurate knowledge of the TPS, attack detection, and real-time system state. In particular, the GAD system features an adaptive mechanism that ensures low false positive and negative rates in detecting the attacks under noisy system measurements. Extensive simulations driven by realistic running profiles of trains verify that a TPS setup is vulnerable to the FDI attacks, but these attacks can be detected effectively by the proposed GAD while ensuring a low false positive rate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
