Important-Data-Based DoS Attack Mechanism and Resilient H_∞ Filter Design for Networked T-S Fuzzy Systems

Area of Science:

• Control Systems Engineering
• Cybersecurity
• Artificial Intelligence

Background:

• Networked Takagi-Sugeno (T-S) fuzzy systems face security vulnerabilities, particularly from denial-of-service (DoS) attacks.
• Existing DoS attack models often lack the sophistication to exploit system information for maximum impact.

Purpose of the Study:

• To propose a novel important-data-based (IDB) DoS attack mechanism targeting networked T-S fuzzy systems.
• To design a resilient H∞ fuzzy filter to mitigate the effects of the proposed IDB DoS attacks.
• To develop a unified attack-defense framework for T-S fuzzy systems with asynchronous premise constraints.

Main Methods:

• An IDB DoS attack mechanism is developed, identifying and targeting critical data packets.
• A resilient H∞ fuzzy filter is designed to counteract the IDB DoS attacks.
• An algorithm is proposed to estimate unknown attack parameters for the defender.
• Lyapunov functional method is employed to establish sufficient conditions for filter design.

Main Results:

• The proposed IDB DoS attack demonstrates enhanced destructiveness by selectively targeting important data.
• The designed resilient H∞ fuzzy filter effectively alleviates the negative impact of the IDB DoS attacks.
• Sufficient conditions for computing filtering gains and ensuring H∞ performance are derived.
• The unified attack-defense framework is validated through two illustrative examples.

Conclusions:

• The novel IDB DoS attack significantly degrades the performance of networked T-S fuzzy systems.
• The developed resilient H∞ fuzzy filter provides an effective defense mechanism against sophisticated DoS attacks.
• The proposed framework offers a comprehensive approach to securing networked T-S fuzzy systems against advanced threats.