Security-guaranteed filter design for discrete-time Markovian jump delayed systems subject to deception attacks and sensor saturation

This work is devoted the problem of a security-guaranteed filter design for a class of discrete-time Markov jump systems that are vulnerable to stochastic deception attacks and have random sensor saturation. Deception attacks, in particular, are taken into account in the filter when the attacker attempts to modify the broadcast signal in communication networks by inserting some misleading information data into the assessment output. The Bernoulli distribution is satisfied by two sets of introduced stochastic variables. It shows the likelihood that the broadcaster’s data transmissions will be the focus of deception attacks and sensor saturation. The Lyapunov functional technique is established, and criteria are derived to ensure that the system is mean-square stable. Furthermore, explicit expression of the filter gains is obtained by solving a set of linear matrix inequalities. Lastly, two simulation examples including a synthetic genetic regulatory network are provided to further demonstrate the validity and efficiency of the suggested theoretical results. •The problem of a security-guaranteed filter design for a class of Markov jump discrete-time systems that are vulnerable to stochastic deception attacks and have random sensor saturation.•Different from the existing literature, stochastic deception attacks and random sensor saturation is occurred simultaneously. The attacker attempts to insert some misleading information data into the measurement output to modify the broadcast signal in the communication networks.•The Bernoulli variables are represents the occurrence probability of the data transmitted by the network being subjected to deception attacks and sensor saturation, respectively.•By establishing the Lyapunov functional technique, criteria are derived to guarantee the system is mean square stable is acquired.•The filter gain matrices can be computed simultaneously using LMI Toolbox in Matlab. Two numerical example including a synthetic genetic regulatory network, provided to demonstrate the effectiveness of the suggested approach.