mlsanityData to 7 Oct 2026

PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT

arXiv 2602.17973 ↗First posted 20 Feb 2026cs.CR cs.AIcited by 0

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Abstract (arXiv)

This paper proposes PenTiDef, a fully decentralized, privacy-preserving, and poisoning-resilient framework for decentralized federated IDS (DFL-IDS). PenTiDef synergistically integrates three key components: (i) client-side Distributed Differential Privacy (DDP) with stochastic Gaussian noise to protect gradient leakage, (ii) a lightweight latent-space defense module that extracts and compresses penultimate-layer representations (PLRs) into stable Latent Semantic Representations (LSRs) via AutoEncoder, followed by Centered Kernel Alignment (CKA) and K-Means clustering for robust malicious update detection without auxiliary datasets, and (iii) a permissioned blockchain layer with smart contracts that orchestrates on-chain validation, secure FedAvg aggregation, and immutable auditability, eliminating any central server. Extensive experiments on CIC-IDS2018 and Edge-IIoTSet under both IID and realistic non-IID settings, with adversary ratios up to 40%, demonstrate that PenTiDef consistently outperforms state-of-the-art baselines (FLARE and FedCC) in detection accuracy and F1-score while maintaining lower training overhead. By jointly addressing privacy, robustness, and decentralization in a unified secure aggregation protocol, PenTiDef provides a practical and scalable solution for trustworthy collaborative intrusion detection in heterogeneous, adversarial IIoT environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 50 references that are in the map. First posted on arXiv 20 Feb 2026.

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19 of 50 references.

References · 19

  1. 2019Deep Leakage from Gradientscited by 1,004
  2. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  4. 2019Robust Aggregation for Federated Learningcited by 314
  5. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
  6. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  7. 2018Distributed Differential Privacy via Shufflingcited by 115
  8. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  9. 2018IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detectioncited by 41
  10. 2022LoMar: A Local Defense Against Poisoning Attack on Federated Learningcited by 29
  11. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
  12. 2024Model Poisoning Attacks to Federated Learning via Multi-Round Consistencycited by 18
7 more
  1. 2020GFL: A Decentralized Federated Learning Framework Based On Blockchaincited by 12
  2. 2023Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systemscited by 10
  3. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  4. 2022FedCC: Robust Federated Learning against Model Poisoning Attackscited by 5
  5. 2024Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networkscited by 5
  6. 2021Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systemscited by 3
  7. 2023Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspectioncited by 1

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