Visible to the public Detection and Mitigation of False Data Injection Attacks for Secure Interactive Networked Control Systems

TitleDetection and Mitigation of False Data Injection Attacks for Secure Interactive Networked Control Systems
Publication TypeConference Paper
Year of Publication2018
AuthorsKubo, Ryogo
Conference Name2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR)
ISBN Number978-1-5386-5547-4
KeywordsAcceleration, bilateral teleoperation systems, Communication channels, composability, control engineering computing, control systems, cyber physical systems, cybersecurity, Delay effects, delays, False Data Detection, false data injection attacks, Force, Human Behavior, Internet, master-to-slave paths, mitigation method, networked control systems, position control, pubcrawl, resilience, Resiliency, robots, secure interactive networked control systems, security of data, slave robots, slave sides, slave-to-master paths, tamper detection observer
Abstract

Cybersecurity in control systems has been actively discussed in recent years. In particular, networked control systems (NCSs) over the Internet are exposed to various types of cyberattacks such as false data injection attacks. This paper proposes a detection and mitigation method of the false data injection attacks in interactive NCSs, i.e., bilateral teleoperation systems. A bilateral teleoperation system exchanges position and force information through the Internet between the master and slave robots. The proposed method utilizes two redundant communication channels for both the master-to-slave and slave-to-master paths. The attacks are detected by a tamper detection observer (TDO) on each of the master and slave sides. The TDO compares the position responses of actual robots and robot models. A path selector on each side chooses the appropriate position and force responses from the responses received through the two communication channels, based on the outputs of the TDO. The proposed method is validated by simulations with attack models.

URLhttps://ieeexplore.ieee.org/document/8535978
DOI10.1109/IISR.2018.8535978
Citation Keykubo_detection_2018