mlsanityData to 7 Oct 2026

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

arXiv 2506.20102 ↗First posted 25 Jun 2025cs.CR cs.LG cs.SYcited by 0

Not on the map. Why?

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 84 references that are in the map. First posted on arXiv 25 Jun 2025.

Builds on

24 of 84 references.

References · 24

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  6. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  7. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  8. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  9. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  10. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  11. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  12. 2018Experience Replay for Continual Learningcited by 1,074
12 more
  1. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  2. 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
  3. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  4. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 243
  5. 2024Continual Learning and Catastrophic Forgettingcited by 73
  6. 2019Ablation Studies in Artificial Neural Networkscited by 58
  7. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  8. 2017Cyber-Physical Systems Security -- A Surveycited by 19
  9. 2024Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregationcited by 13
  10. 2024Personalized Federated Learning via Feature Distribution Adaptationcited by 13
  11. 2023Exploring adversarial attacks in federated learning for medical imagingcited by 8
  12. 2024Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecturecited by 1

Built on by

Not cited yet.