Network Reciprocity Shapes Evolutionary Cybersecurity Dynamics

2026-07-28

AI Summary

This study addresses a key limitation in existing evolutionary cybersecurity models, which often assume homogeneous interactions and thus fail to capture how population structure influences attack–defense dynamics. To overcome this, the authors propose a hybrid-role evolutionary game-theoretic framework that integrates stochastic evolutionary dynamics with large-scale agent-based simulations to systematically investigate how network topology shapes the long-term evolution of attack and defense strategies in AI-assisted security contexts. The work reveals, for the first time, that network reciprocity can spontaneously promote defensive dominance across a broader parameter range and foster resilient clusters that suppress attacks—without requiring external incentives. Results demonstrate that structured populations strongly favor defense, whereas well-mixed populations lead to strategy coexistence, highlighting how local interactions substantially enhance long-term systemic security resilience.

Abstract

AI-assisted cybersecurity systems are characterised by continuous adaptation between attackers and defenders, making evolutionary game theory a natural framework for studying their long-term behaviour. However, existing evolutionary cybersecurity models have primarily focused on homogeneous interactions, providing limited understanding of how population structure influences cyber attack-defence dynamics. In this paper, we develop a mixed-role evolutionary game in which adaptive cyber agents can exhibit both offensive and defensive behaviours, and investigate its dynamics in well-mixed and structured populations. The proposed framework combines stochastic evolutionary analysis with large-scale agent-based simulations to examine how interaction structure shapes long-run strategic behaviour. Our results reveal a fundamental difference between global and local interactions. While well-mixed populations exhibit broad coexistence between attacking and defensive strategies, structured populations generate well-defined evolutionary regimes in which secure defensive behaviour becomes dominant over a much larger region of the parameter space. Spatial analysis further shows that neighbouring defenders naturally form resilient clusters that suppress persistent attacks through network reciprocity. These findings demonstrate that interaction structure is a fundamental determinant of AI-assisted cybersecurity evolution and suggest that organising defensive agents through local networked interactions can substantially improve long-term cyber resilience without requiring additional defensive incentives.

Problem

• evolutionary cybersecurity
• population structure
• network reciprocity
• attack-defence dynamics
• structured populations

Innovation

• network reciprocity
• evolutionary game theory
• structured populations
• cybersecurity dynamics
• agent-based simulation