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

arXiv: https://arxiv.org/abs/2604.10427 · HTML: https://mlsanity.com/paper/2604.10427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 12 Apr 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 10 of 10)

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- [Provably Efficient Q-Learning with Low Switching Cost](https://mlsanity.com/paper/1905.12849/) (2019)
- [Safe Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/2106.06239/) (2021)
- [A Simple Reward-free Approach to Constrained Reinforcement Learning](https://mlsanity.com/paper/2107.05216/) (2021)
- [Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality](https://mlsanity.com/paper/2202.06450/) (2022)
- [Large Language Models are Advanced Anonymizers](https://mlsanity.com/paper/2402.13846/) (2024)
- [A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints](https://mlsanity.com/paper/2302.04375/) (2023)
- [DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2402.13291/) (2024)
- [Near-Optimal Adversarial Reinforcement Learning with Switching Costs](https://mlsanity.com/paper/2302.04374/) (2023)

## Abstract (arXiv)

We develop a queueing-theoretic framework to model the temporal evolution of cyber-attack surfaces, where the number of active vulnerabilities is represented as the backlog of a queue. Vulnerabilities arrive as they are discovered or created, and leave the system when they are patched or successfully exploited. Building on this model, we study how automation affects attack and defense dynamics by introducing an AI amplification factor that scales arrival, exploit, and patching rates. Our analysis shows that even symmetric automation can increase the rate of successful exploits. We validate the model using vulnerability data collected from an open source software supply chain and show that it closely matches real-world attack surface dynamics. Empirical results reveal heavy-tailed patching times, which we prove induce long-range dependence in vulnerability backlog and help explain persistent cyber risk. Utilizing our queueing abstraction for the attack surface, we develop a systematic approach for cyber risk mitigation. We formulate the dynamic defense problem as a constrained Markov decision process with resource-budget and switching-cost constraints, and develop a reinforcement learning (RL) algorithm that achieves provably near-optimal regret. Numerical experiments validate the approach and demonstrate that our adaptive RL-based defense policies significantly reduce successful exploits and mitigate heavy-tail queue events. Using trace-driven experiments on the ARVO dataset, we show that the proposed RL-based defense policy reduces the average number of active vulnerabilities in a software supply chain by over 90% compared to existing defense practices, without increasing the overall maintenance budget. Our results allow defenders to quantify cumulative exposure risk under long-range dependent attack dynamics and to design adaptive defense strategies with provable efficiency.