![[2602.17973] PenTiDef: Enhancing Privacy and Robustness in Decentralized Federated Intrusion Detection Systems against Poisoning Attacks](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17973] PenTiDef: Enhancing Privacy and Robustness in Decentralized Federated Intrusion Detection Systems against Poisoning Attacks
Summary
The paper presents PenTiDef, a novel framework designed to enhance privacy and robustness in decentralized federated intrusion detection systems (DFL-IDS) against poisoning attacks, addressing gaps in existing defenses.
Why It Matters
As federated learning becomes more prevalent in intrusion detection systems, the risks associated with data privacy and poisoning attacks increase. PenTiDef offers a solution that enhances security in decentralized environments, making it relevant for organizations aiming to protect sensitive data while leveraging distributed learning.
Key Takeaways
- PenTiDef improves privacy and robustness in decentralized federated intrusion detection systems.
- The framework uses Distributed Differential Privacy to safeguard data confidentiality.
- It employs a blockchain-based mechanism for decentralized model aggregation and trust enforcement.
- Experimental results indicate superior performance compared to existing defenses like FLARE and FedCC.
- PenTiDef is scalable and effective against real-world attacks from untrustworthy participants.
Computer Science > Cryptography and Security arXiv:2602.17973 (cs) [Submitted on 20 Feb 2026] Title:PenTiDef: Enhancing Privacy and Robustness in Decentralized Federated Intrusion Detection Systems against Poisoning Attacks Authors:Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan, Luong Ha Tien, Ngo Duc Hoang Son, Van-Hau Pham View a PDF of the paper titled PenTiDef: Enhancing Privacy and Robustness in Decentralized Federated Intrusion Detection Systems against Poisoning Attacks, by Phan The Duy and 5 other authors View PDF HTML (experimental) Abstract: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 ...
