Paper Info
Title
Detection Of Sensor Attacks In Uncertain Stochastic Linear Systems
Abstract
A novel attack detection scheme is developed for linear discrete-time systems with unknown dynamics that are subject to the additive process and output measurements noise. A novel stochastic adaptive observer is proposed to estimate the state vector in the presence of noisy sensor measurements and uncertain dynamics, and also to generate the innovation signal to detect attacks using a modified chi(2) detector. It has been shown that the innovation signal, which is defined as the difference between the measured and the estimated output from the observer, has a Gaussian distribution with non-zero mean. The modified chi(2) detector uses the steady-state bound of the innovation signal. Not only this detector can detect false data injection attacks, but also sophisticated attacks like replay attacks can be detected. The learning involved in the system acts as a watermarking signal, which helps in the detection of stealthy attacks. Simulation results are presented to support the theoretical claims.
Year
DOI
Venue
2019
10.1109/CCTA.2019.8920410
2019 3RD IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (IEEE CCTA 2019)
Field
DocType
Citations 
Digital watermarking,State vector,Noise measurement,Linear system,Computer science,Algorithm,Gaussian,Observer (quantum physics),Detector,Replay attack
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Chandreyee Bhowmick172.81
Sarangapani Jagannathan2113694.89