The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Outstanding Reviewers

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • RSS Feed

DOI: 10.14569/IJACSA.2023.0140716
PDF

A Hybrid Federated Learning Framework and Multi-Party Communication for Cyber-Security Analysis

Author 1: Fahad Alqurashi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 7, 2023.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The term "Internet of Things" (IoT) describes a global system of electronically linked devices and sensors capable of two-way communication and data sharing. IoT provides various advantages, including improved efficiency and production and lower operating expenses. Concern about data breaches is constantly present, for example, since devices with sensors capture and send confidential data that might have dire effects if leaked. Hence, this research proposed a novel hybrid federated learning framework with multi-party communication (FLbMPC) to address the cyber-security challenges. The proposed approach comprises four phases: data collection and standardization, model training, data aggregation, and attack detection. The research uses the UNSW-NB15 cyber-security dataset, which was collected and standardized using the z-score normalization approach. Federated learning was used to train the local models of each IoT device with their respective subsets of data. The MPC method is used to aggregate the encrypted local models into a global model while maintaining the confidentiality of the local models. Finally, in the attack detection phase, the global model compares real-time sensor data and predicted values to identify cyber-attacks. The experiment findings show that the suggested model outperforms the current methods in terms of accuracy, precision, f-measure and recall.

Keywords: Federated learning; multi-party communication; cyber-security; machine learning; internet of things

Fahad Alqurashi. “A Hybrid Federated Learning Framework and Multi-Party Communication for Cyber-Security Analysis”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.7 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140716

@article{Alqurashi2023,
title = {A Hybrid Federated Learning Framework and Multi-Party Communication for Cyber-Security Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140716},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140716},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Fahad Alqurashi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

The Science and Information (SAI) Organization Limited is a company registered in England and Wales under Company Number 8933205.