Detection and Isolation of False Data Injection Attack in Intelligent Transportation System via Robust State Observer

Abstract

As the future of transportation systems, the intelligent transportation system is a promising technology to improve the increasingly serious traffic problems. However, the integration of cyber-physical systems makes them vulnerable to new cyber–physical attacks. To ensure the security of intelligent transportation systems, a novel robust state observer-based detection and isolation method against false data injection attacks is developed. Based on the constructed dynamic model of intelligent vehicle networking, the covert characteristic of a false data injection attack is analyzed. Then, a novel state residuals-based detection criterion is developed by using a real-time observed state. To shorten the detection time, an adaptive detection threshold is designed to replace the existing computed threshold. In addition, robust state observer banks are established to isolate multiple injected attacks. Finally, simulation results on the vehicle networking system demonstrate the effectiveness of the developed detection and isolation method against false data injection attacks.

Adaptive unknown input observer-based detection and identification method for intelligent transportation under malicious attack

“…Simulation part parameters are given: ζ i = 0 . 25 , d d = 3 m , d i + 1 − d i = 40 m , v 1 = 20 km / h , where the adaptive gains of each controller on the intelligent vehicle networking system are taken from. 33 Under the given related parameters, the following two situations are considered. Based on the works, 39 we can set the precomputed threshold to 3 τ .…”

“…Assuming that hacker can inject the single FDIA into the third intelligent vehicle at t ≥ 60 s , where the attack vector is given as Huang and Wang. 33 By using the proposed Algorithm 1, a bank of parallel AUIOs are designed. As shown in Figure 4, the corresponding change of attack residuals can be obtained.…”

“…It is obvious that the error of state estimation under adaptive unknown input observers is smaller than that under robust state observers. Compared with the UIO-based detection work in Huang and Wang 33 and Yamamoto et al, 34 the performance of state estimation error can be shortened. Furthermore, the false positive rate under the precomputed threshold is used to evaluate the detection performance against FDIA, as shown in Figure 6(b).…”

“…However, the above learning-based detection methods relay on the training of historical data. Thus, a physical dynamic model-based detection methods against FDIAs were proposed in Huang and Wang 33 and Yamamoto et al 34 In Huang and Wang, 33 a robust state observer-based detection method against FDIAs was proposed. Yamamoto et al 34 developed an UIO-based detection method against FDIAs in vehicle networking system based on a state residuals.…”

“…Yamamoto et al 34 developed an UIO-based detection method against FDIAs in vehicle networking system based on a state residuals. Although the above detection works in Huang and Wang 33 and Yamamoto et al 34 can reduce the complexity of attack detection, the accuracy of state estimation limits the detection performance of FDIAs. In addition, the existing detection works lack the research on the detection and identification of multiple attacks.…”