# Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox

arXiv: https://arxiv.org/abs/2506.20102 · HTML: https://mlsanity.com/paper/2506.20102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 84 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [Continual Learning and Catastrophic Forgetting](https://mlsanity.com/paper/2403.05175/) (2024)
- [Ablation Studies in Artificial Neural Networks](https://mlsanity.com/paper/1901.08644/) (2019)
- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [Cyber-Physical Systems Security -- A Survey](https://mlsanity.com/paper/1701.04525/) (2017)
- [Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation](https://mlsanity.com/paper/2409.01435/) (2024)
- [Personalized Federated Learning via Feature Distribution Adaptation](https://mlsanity.com/paper/2411.00329/) (2024)
- [Exploring adversarial attacks in federated learning for medical imaging](https://mlsanity.com/paper/2310.06227/) (2023)
- [Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture](https://mlsanity.com/paper/2412.00687/) (2024)

## Abstract (arXiv)

The convergence of Information Technology and Operational Technology has exposed Industrial Control Systems to adaptive, intelligent adversaries that render static defenses obsolete. This paper introduces the Adversarial Resilience Co-evolution (ARC) framework, addressing the "Trinity of Trust" comprising model fidelity, data integrity, and analytical resilience. ARC establishes a co-evolutionary arms race within a Fortified Secure Digital Twin (F-SCDT), where a Deep Reinforcement Learning "Red Agent" autonomously discovers attack paths while an ensemble-based "Blue Agent" is continuously hardened against these threats. Experimental validation on the Tennessee Eastman Process (TEP) and Secure Water Treatment (SWaT) testbeds demonstrates superior performance in detecting novel attacks, with F1-scores improving from 0.65 to 0.89 and detection latency reduced from over 1200 seconds to 210 seconds. A comprehensive ablation study reveals that the co-evolutionary process itself contributes a 27% performance improvement. By integrating Explainable AI and proposing a Federated ARC architecture, this work presents a necessary paradigm shift toward dynamic, self-improving security for critical infrastructure.