Exponential synchronization of coupled neural networks under stochastic deception attacks

Area of Science:

• Control Theory
• Cybersecurity
• Computational Neuroscience

Background:

• Coupled neural networks are crucial for complex computations.
• Stochastic deception attacks pose significant threats to network security and performance.
• Ensuring synchronization in neural networks under adversarial conditions is a key challenge.

Purpose of the Study:

• To investigate synchronization in coupled neural networks under stochastic deception attacks.
• To develop theoretical criteria for achieving synchronization in compromised networks.
• To analyze the impact of stochastic impulses on network synchronization.

Main Methods:

• Development of a general differential inequality with delayed impulses.
• Extension of the inequality to handle delayed stochastic impulses.
• Modeling deception attacks as stochastic impulses.
• Derivation of synchronization criteria using Lyapunov stability theory.

Main Results:

• Established a novel differential inequality for systems with delayed stochastic impulses.
• Provided synchronization criteria for coupled neural networks under stochastic deception attacks.
• Demonstrated the effectiveness of the proposed methods through two numerical examples.

Conclusions:

• The derived synchronization criteria are effective for coupled neural networks under stochastic deception attacks.
• The theoretical results provide a foundation for designing robust and secure neural network systems.
• Further research can explore adaptive control strategies for enhanced resilience.