On Situational Aware En-Route Filtering against Injected False Data in Cyber Physical Systems

Abstract

Cyber-physical systems (CPS) are systems with a tight coupling of the cyber aspects of computing and communications with the physical aspects of dynamics and engineering that abide by the laws of physics. The real-time monitoring provided by wireless sensor networks (WSNs) is essential for CPS, as it provides rich and pertinent information on the condition of physical systems. In WSNs, the attackers could inject false measurements to the controller through compromised sensor nodes, which not only threaten the security of the system, but also consume significant network resources and pose serious threats to the lifetime of sensor networks. To mitigate false data injection (FDI) measurement attacks, a number of situation aware en-route filtering schemes to filter false data inside the networks have been developed. In this book chapter, the authors first review those existing situation aware en-route filter mechanisms such as: Statistical En-route Filtering (SEF), Location-Based Resilient Secrecy (LBRS), Location-ware End-to-end Data Security (LEDS), and Dynamic En-route Filtering Scheme (DEFS). The authors then compare the performance of those schemes via both the theoretical analysis and simulation study. These extensive simulations validate findings that most of the schemes can filter out false data within few hops, and the filtering efficiency increases as the number of hops increases and the filtering efficiency of most schemes decreases rapidly as the number of compromised nodes increases.