Robustness of network attack strategies against node sampling and link errors

Area of Science:

• Network Science
• Cybersecurity
• Complex Systems Analysis

Background:

• Existing network attack strategies often assume complete network knowledge, which is unrealistic for large-scale systems.
• Real-world network information is frequently imperfect due to measurement errors or sampling limitations.

Purpose of the Study:

• To evaluate the resilience of network attack strategies under conditions of imperfect network information.
• To compare the impact of different types of information imperfections (link errors vs. sampling) on attack effectiveness.

Main Methods:

• Extensive computer simulations were conducted to model network attack scenarios.
• Two imperfect information scenarios were simulated: networks with link errors and networks derived from sampling.
• Attack strategy effectiveness was analyzed across various network structures, particularly those with skewed degree distributions.

Main Results:

• Network attack strategies demonstrate significant robustness against link errors, maintaining effectiveness even with up to 30% inaccuracies.
• Attack effectiveness significantly decreases with random network sampling.
• Biased sampling strategies (e.g., Breadth-First Search, Depth-First Search) mitigate the negative impact of sampling on attack strategies.

Conclusions:

• Network attack strategies are surprisingly resilient to measurement errors in network topology.
• The method of acquiring network information (sampling technique) critically influences the success of network attacks.
• Findings have implications for network defense and understanding the vulnerabilities of large-scale networks.