PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT
Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan, Luong Ha Tien, Ngo Duc Hoang Son, Van-Hau Pham

TL;DR
PenTiDef is a decentralized, privacy-preserving federated intrusion detection framework for IIoT that combines differential privacy, latent-space defense, and blockchain coordination, demonstrating superior detection accuracy and robustness.
Contribution
It introduces a novel integrated framework combining client-side differential privacy, latent-space malicious update detection, and blockchain-based secure aggregation for IIoT intrusion detection.
Findings
Outperforms state-of-the-art baselines in detection accuracy and F1-score.
Maintains lower training overhead compared to existing methods.
Effective under both IID and non-IID data distributions with high adversary ratios.
Abstract
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…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Privacy-Preserving Technologies in Data · Adversarial Robustness in Machine Learning
