Simulating Advanced Social Botnets: A Framework for Behavior Realism and Coordinated Stealth

The increasing sophistication of social bots demands advanced simulation frameworks to model potential vulnerabilities in detection systems and probe their robustness.While existing studies have explored aspects of social bot simulation, they often fall short in capturing key adversarial behaviors. To address this gap, we propose a simulation framework that jointly incorporates both realistic behavioral mimicry and adaptive inter-bot coordination. Our approach introduces a human-like behavior module that reduces detectable divergence from genuine user activity patterns through distributional matching, combined with a coordination module that enables strategic cooperation while maintaining structural stealth. The effectiveness of the proposed framework is validated through adversarial simulations against both feature-based (Random Forest) and graph-based (BotRGCN) detectors on a real-world dataset. Experimental results demonstrate that our approach enables bots to achieve remarkable evasion capabilities, with the human-like behavior module reaching up to a 100% survival rate against RF-based detectors and 99.1% against the BotRGCN detector. This study yields two key findings: (1) The integration of human-like behavior and target-aware coordination establishes a new paradigm for simulating botnets that are resilient to both feature-based and graph-based detectors; (2) The proposed likelihood-based reward and group-state optimization mechanism effectively align botnet activities with the social context, achieving concealment through integration rather than mere avoidance. The framework provides valuable insights into the complex interplay between evasion strategies and detector effectiveness, offering a robust foundation for future research on social bot threats.