mlsanityData to 7 Oct 2026

WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems

arXiv 2504.04367 ↗First posted 6 Apr 2025cs.CR cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

In the era of data expansion, ensuring data privacy has become increasingly critical, posing significant challenges to traditional AI-based applications. In addition, the increasing adoption of IoT devices has introduced significant cybersecurity challenges, making traditional Network Intrusion Detection Systems (NIDS) less effective against evolving threats, and privacy concerns and regulatory restrictions limit their deployment. Federated Learning (FL) has emerged as a promising solution, allowing decentralized model training while maintaining data privacy to solve these issues. However, despite implementing privacy-preserving technologies, FL systems remain vulnerable to adversarial attacks. Furthermore, data distribution among clients is not heterogeneous in the FL scenario. We propose WeiDetect, a two-phase, server-side defense mechanism for FL-based NIDS that detects malicious participants to address these challenges. In the first phase, local models are evaluated using a validation dataset to generate validation scores. These scores are then analyzed using a Weibull distribution, identifying and removing malicious models. We conducted experiments to evaluate the effectiveness of our approach in diverse attack settings. Our evaluation included two popular datasets, CIC-Darknet2020 and CSE-CIC-IDS2018, tested under non-IID data distributions. Our findings highlight that WeiDetect outperforms state-of-the-art defense approaches, improving higher target class recall up to 70% and enhancing the global model's F1 score by 1% to 14%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 65 references that are in the map. First posted on arXiv 6 Apr 2025.

Builds on

19 of 65 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2018How To Backdoor Federated Learningcited by 745
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  6. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  7. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  8. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  9. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  10. 2018Mitigating Sybils in Federated Learning Poisoningcited by 158
  11. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  12. 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
7 more
  1. 2021CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detectioncited by 64
  2. 2019Federated Learning with Bayesian Differential Privacycited by 60
  3. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  4. 2021Federated Learning for Malware Detection in IoT Devicescited by 36
  5. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
  6. 2022FL-Defender: Combating Targeted Attacks in Federated Learningcited by 12
  7. 2025Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Casecited by 1

Built on by

Not cited yet.