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Federated Learning and Data Mining-Based Botnet Attack Detection Framework for Internet of Things.

Kalupahana Liyanage Kushan Sudheera1, Lokuge Lehele Gedara Madhuwantha Priyashan1, Oruthota Arachchige Sanduni Pavithra1

  • 1Department of Electrical and Information Engineering, Faculty of Engineering, University of Ruhuna, Galle 80000, Sri Lanka.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces FDA, a federated learning framework for detecting multi-stage botnet attacks in Internet of Things (IoT) networks. It enables privacy-preserving, collaborative detection of evolving threats across distributed environments.

Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning

Background:

  • Internet of Things (IoT) environments face sophisticated, multi-stage botnet attacks.
  • Centralized detection methods struggle with data heterogeneity, imbalance, scalability, and privacy concerns in distributed IoT networks.

Purpose of the Study:

  • To propose FDA, a federated learning-based and data mining-driven framework for stage-aware botnet attack detection in IoT networks.
  • To enable privacy-preserving, collaborative detection of evolving botnet attack patterns across distributed IoT networks.

Main Methods:

  • FDA utilizes federated learning and data mining at network gateways for anomaly detection.
  • Frequent Itemset Mining (FIM) abstracts anomalous traffic into compact, interpretable patterns.
  • Lightweight neural networks are trained locally, with global model aggregation performed via federated learning, avoiding raw data sharing.
Keywords:
botnet attackcyber-securitydata miningfederated learninginternet of thingsmachine learning

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Main Results:

  • FDA achieved anomaly detection F1-scores exceeding 99% across all gateways.
  • Multi-stage botnet attack classification F1-scores ranged from 48-49%, comparable to centralized methods.
  • The framework operated effectively under decentralized and privacy-preserving constraints.

Conclusions:

  • FDA offers a practical and effective solution for distributed botnet attack stage detection in IoT.
  • The framework enhances collaborative learning of evolving threats while preserving user privacy.
  • FDA demonstrates the viability of federated learning for robust cybersecurity in heterogeneous IoT ecosystems.