  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.
