A Critical Candidate Node-Based Attack Model of Network Controllability.

Area of Science:

• Network Science
• Systems Engineering
• Cybersecurity

Background:

• Controllability is a fundamental property of complex networks.
• Assessing network robustness against destructive attacks is crucial for practical applications.
• Understanding how malicious attacks impact network controllability is an active research area.

Purpose of the Study:

• To propose and evaluate a novel attack model for assessing network controllability under malicious attacks.
• To compare the proposed model against existing attack strategies in terms of effectiveness and efficiency.
• To identify critical nodes essential for maintaining network controllability and enhancing resilience.

Main Methods:

• Development of a novel attack model to precisely identify and target critical candidate nodes.
• Comparative analysis against established attack methods: degree-based, betweenness-based, closeness-based, PageRank-based, and hierarchical attacks.
• Extensive experimentation on both synthetic and real-world network datasets.

Main Results:

• The proposed attack model demonstrates superior disruption effectiveness compared to traditional methods.
• The novel model exhibits higher computational efficiency.
• Experimental validation confirms the model's superior performance across diverse network types.

Conclusions:

• The novel attack model provides a precise and efficient means to challenge network controllability.
• Identifying critical nodes through this model is key to understanding and enhancing network resilience.
• This research offers a robust framework for defending complex networks against malicious attacks.