Collaborative Botnet Detection in Heterogeneous Devices of Internet of Things using Federated Deep Learning

Aulia Arif Wardana, Parman Sukarno, Muhammad Salman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This research introduces a pioneering approach, termed Hierarchical Collaborative Botnet Detection, leveraging Federated Deep Learning to address the escalating security concerns within the Internet of Things (IoT) ecosystems characterized by heterogeneous devices. The proposed framework establishes a hierarchical structure facilitating efficient collaboration among devices at different levels, enabling scalable and distributed botnet detection. Federated Deep Learning ensures model training without centralizing sensitive data, respecting privacy constraints inherent in IoT environments. The methodology involves the development of a collaborative learning model capable of analyzing diverse data sources across the IoT landscape, utilizing the N-BaIoT dataset for comprehensive evaluation. Comprehensive simulations and experiments, conducted with the N-BaIoT dataset, showcase the robustness and efficiency of the proposed approach in detecting botnet activities across diverse IoT devices. Based on experimental results, the proposed method can identify botnets with an average accuracy of 98,97, precision of 98,75, recall of 99,41, and an F1-score of 99,11. The hierarchical and federated nature of the model contributes to a more resilient and scalable botnet detection system for large-scale IoT deployments, laying the foundation for a secure and collaborative IoT landscape in the face of evolving cyber threats.

Original languageEnglish
Title of host publicationICSCA 2024 - 2024 13th International Conference on Software and Computer Applications
PublisherAssociation for Computing Machinery
Pages287-291
Number of pages5
ISBN (Electronic)9798400708329
DOIs
Publication statusPublished - 1 Feb 2024
Event13th International Conference on Software and Computer Applications, ICSCA 2024 - Bali Island, Indonesia
Duration: 1 Feb 20243 Feb 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Software and Computer Applications, ICSCA 2024
Country/TerritoryIndonesia
CityBali Island
Period1/02/243/02/24

Keywords

  • deep learning
  • federated learning
  • heterogeneous devices
  • intrusion detection
  • IoT

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