# Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems
## 5 Conclusions  
This paper described the sensor attack detection and vehicle speed estimation method. The sensor attack detection method to use RNN was suggested and it focuses on the wheel speed sensors. The vehicle speed estimation method without an additional sensor is studied for data integrity of the wheel speed sensors. We firstly defined classes and divided a driving situation into traction and braking. WA to calculate different weighted values of wheels relying on the classes and situations was applied in the vehicle speed estimation. Attack detection and identification were performed with a high probability, and MSE of the proposed system was lower than those of other algorithms. Attack detection and identification is a necessary process since vehicle speed is calculated with only normal data. The high classification accuracy is required to correctly estimate the vehicle speed. Moreover,  
Figure 10: Comparison of fusion algorithms during braking: (**a**) Braking; (**b**) Vehicle speed and estimated speed; (**c**) Vehicle speed and wheel speed to inject false data into.  
the modified WA to show the lowest MSE has relatively high integrity. In other words, exact speed information means high integrity. It turned out that the system used the modified WA is safe because high integrity is related to the life of drivers and pedestrians.  
Our system is applicable to a diagnosis system in CPS. If the diagnosis system is applied when an attack occurs on the wheel speed sensors in automotive CPS, the estimated vehicle speed information instead of vehicle speed calculated with the false wheel speed data will be transmitted to a controller. The controller may control the vehicle based on the information, and the system would contribute to the safety of drivers and pedestrians. In addition to wheel speed sensors of the vehicle, it is possible to apply our system to other sensors of the vehicle or sensors of other platforms. In the future, an online learning method is needed in case untrained data is input as new data. In addition, it is necessary to expand the system after experimenting with various environments including left and right turn and other road surfaces. We have a plan to investigate reinforcement learning and WA with not constant but adaptive weighted values to apply the proposed system to vehicles with different data patterns and overcome the above limitations.  
Conceptualization, J.S.; methodology, J.S.; software, J.S.; validation, J.S., Y.B.; formal analysis, J.S., Y.B., J.L. and S.L.; investigation, J.S. and Y.B.; resources, J.S., J.L and S.L.; data curation, J.S., writing--original draft preparation, J.S. and Y.B.; writing--review and editing, J.S. and Y.B.; visualization, J.S.; supervision, J.S. and Y.B.; project administration, Y.B. and S.L.; funding acquisition, Y.B. and S.L.  
Funding: This research was supported in part by Global Research Laboratory Program (Grant No.: 2013K1A1A2A02078326) through the NRF of Korea, DGIST Research and Development Program funded by the Ministry of Science, ICT and Future Planning (Grand No.: 18-IT-01), and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2014-0-00065, Resilient Cyber-Physical Systems Research).  
The authors declare no conflict of interest.