A novel replay attack detection approach for uncertain systems

Abstract An innovative detection technique for replay attacks in discrete‐time systems with uncertain parameters and bounded noises is presented. Utilizing the interval model provides a novel approach to handling uncertain parameters, which significantly lessens the impact of uncertainty. The dynamic event‐triggered algorithm based on ellipsoid is created to decrease redundant data transmission. It can intelligently adjust the trigger conditions and affect the update of set‐membership estimation. On this basis, a new ellipsoidal set‐membership estimator is formulated to predict the state of the linear parameter‐varying system when subjected to bounded noises. Subsequently, an auxiliary separation function is brought in to establish the detection scheme for replay attacks. The existence of the replay attack can be confirmed by determining whether the residual signal falls inside the residual ellipsoid. Unlike other existing detection methods, the established detection framework can detect replay attacks with 100% accuracy. Finally, the effectiveness and superiority of the designed strategy are further demonstrated by the simulation example of the direct current motor.