Intermittent bias and state filtering for linear systems subject to deception attacks in communication networks

Abstract

This paper presents a state filtering scheme for linear stochastic discrete-time systems subject to random deception attacks on the control signals of a controlled plant. When these attacks are detected inside the network and blocked with a one period time delay, we show that the state model of the plant viewed by the controller is affected by an intermittent bias of unknown initial condition. The intermittent bias and state filtering is obtained by treating the initial state of the intermittent bias as intermittent unknown inputs. The stochastic stability conditions of the proposed filter are established when the arrival binary sequence of attacks follows a Bernoulli random process.