利用物理与社会数据间冗余的社会技术交通系统网络攻击检测
Cyber-Attack Detection in Socio-Technical Transportation Systems Exploiting Redundancies Between Physical and Social Data
AI总结：
针对智能交通系统，提出结合物理与社会数据双并行PDE滤波器的网络攻击检测方案，通过比较输出残差判断攻击，并经Lyapunov稳定性分析与仿真验证有效性。
摘要：
由于人类用户与技术系统之间日益增加的交互，网络-物理-社会连接是智能交通系统（ITSs）的关键要素。这种连接将ITSs转化为社会技术性质的动态系统。利用这一社会技术特征，我们提出了一种针对ITSs的网络攻击检测方案，重点关注针对高速公路交通基础设施的网络攻击。该方案结合了两个并行的基于宏观交通模型的偏微分方程（PDE）滤波器，通过比较其输出残差来决定攻击是否发生。其中一个滤波器利用物理（车辆/基础设施）传感器数据作为反馈，而另一个利用来自人类用户移动设备的社会数据作为反馈。基于社会数据的滤波器由虚假数据隔离器和社会信号处理器辅助，后者将社会信息转化为可用的反馈信号。利用Lyapunov稳定性理论分析了滤波器的数学收敛性。最后，我们通过给出仿真结果验证了所提出的方案。
英文摘要：
Cyber-physical-social connectivity is a key element in Intelligent Transportation Systems (ITSs) due to the ever-increasing interaction between human users and technological systems. Such connectivity translates the ITSs into dynamical systems of socio-technical nature. Exploiting this socio-technical feature to our advantage, we propose a cyber-attack detection scheme for ITSs that focuses on cyber-attacks on freeway traffic infrastructure. The proposed scheme combines two parallel macroscopic traffic model-based Partial Differential Equation (PDE) filters whose output residuals are compared to make decision on attack occurrences. One of the filters utilizes physical (vehicle/infrastructure) sensor data as feedback whereas the other utilizes social data from human users' mobile devices as feedback. The Social Data-based Filter is aided by a fake data isolator and a social signal processor that translates the social information into usable feedback signals. Mathematical convergence properties are analyzed for the filters using Lyapunov's stability theory. Lastly, we validate our proposed scheme by presenting simulation results.
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