Jove
Visualize
Contact Us

Related Experiment Video

Updated: Jun 29, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

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

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Novel Cooperative AI-Based Fall Risk Prediction Model for Older Adults.

Sensors (Basel, Switzerland)·2025
Same author

Facial Anti-Spoofing Using "Clue Maps".

Sensors (Basel, Switzerland)·2024
Same author

Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios.

Sensors (Basel, Switzerland)·2024
Same author

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

Sensors (Basel, Switzerland)·2024
Same author

An Adaptive Traffic-Flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons.

Sensors (Basel, Switzerland)·2023
Same author

An Optimization Framework for Data Collection in Software Defined Vehicular Networks.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
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

Related Experiment Videos

Last Updated: Jun 29, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

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.