# DYNAS: A Dynamic Network Penetration Testing Framework Using Deep Hierarchical Reinforcement Learning

**Authors:** Md. Rakibul Hassan, Majedul Islam, Md. Ashraful Alam, Muhammad Iqbal Hossain, Swakkhar Shatabda  
**Lab:** BRAC University · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.40214) · [PDF](https://arxiv.org/pdf/2610.40214) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40214.html)

## Abstract

As network scale and uncertainty complicate automated penetration testing, reinforcement learning (RL) environments must support controlled evaluation under defensive and non-stationary conditions. We present DYNAS (Dynamic Network Attack Simulator), which combines parameterized large-scale scenario generation with stateful intrusion detection responses, explicit scan false-positive and false-negative noise, temporary service unavailability, and duration-based action timeouts. We further introduce Pen-DHRL, a recurrent manager--worker policy that selects persistent subgoals using IDS-conditioned global context. We compare Pen-DHRL with GNN, Invariant, and Attention baselines across four scenario variants and multiple training regimes. Under IDS-disabled training followed by IDS-enabled evaluation, Pen-DHRL obtains approximately 64\% and 24\% higher reward per step on Bridge and Dynamic, respectively, and approximately 26\% and 30\% more captured hosts on VarA and VarB than the next-best baseline. With all defensive dynamics active from the beginning of training, Pen-DHRL achieves positive mean episode returns in all four scenarios, whereas the flat baselines produce negative mean returns in all 12 architecture--scenario combinations; staged curriculum training produces positive mean returns across all 16 evaluated combinations.

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