What question did this study set out to answer?

The aim is to develop a framework that mitigates network attacks in distributed systems while preserving user privacy.

January 26, 2026Open Access

A privacy-preserving framework for mitigating network attacks in distributed systems using FedGen-AI

PGPeenal GuptaHeidelberg University BSBanhirup SenguptaTata Institute of Fundamental Research BVBinh VuUniversity of Hagen

Key Points

The aim is to develop a framework that mitigates network attacks in distributed systems while preserving user privacy.
Developed a three-tiered architecture: Cloud, Edge, IoT.
Utilized Federated Transfer Learning (FTL) for collaborative training of a global intrusion detection model.
Implemented Generative AI to create novel attack data for improved detection capabilities.
Employed Blockchain to secure the integrity of the federated learning process.
Achieved detection accuracy of up to 92% and an F1-score exceeding 80%.
Demonstrated robust performance with stable convergence over 20 aggregation rounds.
Confirmed adaptability to diverse domains without centralizing sensitive data.

Abstract

Purpose This study aims to address critical security gaps in distributed systems by developing and evaluating a novel framework that integrates Federated Transfer Learning (FTL), Generative AI (GenAI) and Blockchain technology. Research specifically enhances the detection and mitigation of sophisticated network attacks, including distributed denial of service, man-in-the-middle and model poisoning. Design/methodology/approach The study uses a quantitative evaluation-based approach. The authors propose a three-tiered (Cloud, Edge, IoT) architecture in which edge devices collaboratively train a global intrusion detection model using FTL, ensuring data privacy. GenAI synthesises novel attack data, significantly improving the model’s capability to detect zero-day threats that conventional methods may miss. Blockchain technology secures the integrity of the federated learning process, using a reputation-based mechanism to safeguard against malicious contributions and model poisoning attacks. The framework’s performance is rigorously validated using four public NetFlow-based data sets. NF-ToN-IoT-v2, NF-CSECIC-IDS2018-v2, NF-UNSW-NB15-v2 and NF-BoT-IoT-v2. Findings The experimental results demonstrate the high efficacy of the framework. It achieved a detection accuracy of up to 92% and an F1-score exceeding 80% on client nodes, showing robust performance across heterogeneous and non-IID data distributions (Zhao, 2020; Nguyen et al., 2021). The federated model exhibited stable convergence over 20 aggregation rounds, confirming its adaptability and ability to generalise effectively across diverse domains without centralising sensitive data. The integration of GenAI and Blockchain substantially enhanced the model’s robustness, adaptability and trustworthiness. Originality/value This research presents a novel, holistic security solution synergistically combining FTL, GenAI and Blockchain. Its originality lies in its integrated architecture that simultaneously addresses data privacy, model integrity and adaptability to evolving threats. The findings offer a practical blueprint for creating scalable, privacy-conscious and resilient security frameworks applicable to complex distributed environments.

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