The increasing deployment of Federated Learning (FL) in Intrusion Detection Systems (IDS) introduces new challenges related to data privacy, centralized coordination, and susceptibility to poisoning attacks. While significant research has focused on protecting traditional FL-IDS with centralized aggregation servers, there remains a notable gap in addressing the unique challenges of decentralized FL-IDS (DFL-IDS). This study aims to address the limitations of traditional centralized FL-IDS by proposing a novel defense framework tailored for the decentralized FL-IDS architecture, with a focus on privacy preservation and robustness against poisoning attacks. We propose PenTiDef, a privacy-preserving and robust defense framework for DFL-IDS, which incorporates Distributed Differential Privacy (DDP) to protect data confidentiality and utilizes latent space representations (LSR) derived from neural networks to detect malicious updates in the decentralized model aggregation context. To eliminate single points of failure and enhance trust without a centralized aggregation server, PenTiDef employs a blockchain-based decentralized coordination mechanism that manages model aggregation, tracks update history, and supports trust enforcement through smart contracts. Experimental results on CIC-IDS2018 and Edge-IIoTSet demonstrate that PenTiDef consistently outperforms existing defenses (e.g., FLARE, FedCC) across various attack scenarios and data distributions. These findings highlight the potential of PenTiDef as a scalable and secure framework for deploying DFL-based IDS in adversarial environments. By leveraging privacy protection, malicious behavior detection in hidden data, and working without a central server, it provides a useful security solution against real-world attacks from untrust participants.

翻译：随着联邦学习在入侵检测系统中的日益广泛应用，数据隐私、中心化协调以及易受投毒攻击等新挑战随之出现。尽管已有大量研究致力于保护采用中心化聚合服务器的传统联邦学习入侵检测系统，但在应对去中心化联邦学习入侵检测系统的独特挑战方面仍存在显著空白。本研究旨在通过提出一种专为去中心化联邦学习入侵检测系统架构设计的新型防御框架，以解决传统中心化联邦学习入侵检测系统的局限性，并重点关注隐私保护与抵御投毒攻击的鲁棒性。我们提出了PenTiDef——一个面向去中心化联邦学习入侵检测系统的隐私保护鲁棒防御框架。该框架融合分布式差分隐私以保护数据机密性，并利用神经网络衍生的潜在空间表示在去中心化模型聚合场景中检测恶意更新。为消除单点故障并在无中心化聚合服务器的情况下增强信任，PenTiDef采用基于区块链的去中心化协调机制，该机制负责管理模型聚合、追踪更新历史，并通过智能合约支持信任执行。在CIC-IDS2018和Edge-IIoTSet数据集上的实验结果表明，PenTiDef在多种攻击场景与数据分布下均持续优于现有防御方案（如FLARE、FedCC）。这些发现凸显了PenTiDef作为可扩展安全框架，在对抗性环境中部署基于去中心化联邦学习的入侵检测系统的潜力。通过融合隐私保护机制、隐藏数据中的恶意行为检测技术以及无中心服务器的运行模式，该框架为防御现实世界中不可信参与者发起的攻击提供了有效的安全解决方案。