A Queueing-Theoretic Framework for Dynamic Attack Surfaces: Data-Integrated Risk Analysis and Adaptive Defense

Researchers develop a queueing-theoretic framework that models cyber-attack surfaces as dynamic systems where vulnerabilities arrive and depart over time. Using reinforcement learning and Markov decision processes, they demonstrate an adaptive defense strategy that reduces active vulnerabilities by over 90% in software supply chains without increasing maintenance budgets.

This research addresses a critical gap in cybersecurity by applying queueing theory and reinforcement learning to model and mitigate vulnerability exposure in dynamic systems. The framework treats vulnerabilities as arrivals and departures in a queue, capturing the temporal nature of attack surfaces where threats accumulate faster than organizations can patch them. The key finding—that even symmetric automation between attack and defense can favor attackers—reveals a counterintuitive risk in AI-driven security operations that deserves attention from security teams deploying automated tooling.

The study's validation against real vulnerability data from open-source software supply chains grounds theoretical insights in practical reality. The discovery of heavy-tailed patching distributions explains why cumulative vulnerability exposure persists despite incremental patch efforts. This long-range dependence phenomenon suggests that traditional patch management approaches fail to account for temporal correlation in vulnerability lifecycles.

The proposed RL-based defense policy represents a meaningful advance for organizations managing sprawling software ecosystems. Reducing active vulnerabilities by 90% without budget increases implies significant efficiency gains through intelligent resource allocation rather than brute-force scaling. This has direct implications for enterprises, cloud infrastructure providers, and open-source ecosystems grappling with supply chain security.

The regret-bounded RL algorithm provides theoretical guarantees on defense strategy performance, bridging academic rigor with practical deployment. Organizations managing large codebases can leverage this framework to shift from reactive patching to predictive vulnerability management. However, implementation complexity and integration with existing security orchestration platforms remains a practical barrier to adoption.

• →Queueing models reveal that automated attack capabilities can outpace automated defense when symmetrically scaled
• →Heavy-tailed patching distributions create long-range dependent vulnerability backlogs that persist despite steady patch rates
• →RL-based adaptive defense reduces active vulnerabilities by 90% compared to conventional practices without budget increases
• →Vulnerability data from software supply chains validates theoretical framework predictions in real-world environments
• →Regret-bounded algorithms provide provably efficient defense strategies under resource and switching cost constraints