Resilient Synchronization for Insecure Markovian Jump Neural Networks to Mitigate Dual Cyber Attacks

This study proposes a resilient asynchronous controller for Markovian jump neural networks, which can be impervious to the dual cyber attacks that act on actuators and sensors. The complicated occasions of uncertain system modes, actuator and sensor attacks, and unknown attack information are all considered. It is known that sensor attacks can generate corrupted signals to destroy the controller, and actuator attacks can maliciously tamper with the control signals. Mindful of such circumstances, a resilient controller is developed to defend against actuator and sensor attacks as well as to guarantee good synchronization performances. To overcome the unknowns of the occurred attacks, some new adaptive laws for adjusting attack parameters are introduced into the controller to assist with offsetting attack-induced influences. Under the designed controller, the synchronization error dynamic system is proven to be ultimately bounded within a known region, and then the obtained results are extended to address some other cases. Furthermore, a practical example of an analog resistance-capacitance network circuit and some comparative studies are demonstrated to verify the feasibility and superiority of the proposed controller.