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ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques

PeerJ Computer Science, ISSN: 2376-5992, Vol: 10, Page: e2001
2024
  • 0
    Citations
  • 0
    Usage
  • 5
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Captures
    5
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

New Findings Reported from University of Jeddah Describe Advances in Computer Science (ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques)

2024 MAY 09 (NewsRx) -- By a News Reporter-Staff News Editor at Computer News Today -- Investigators publish new report on computer science. According to

Article Description

This study focuses on addressing computational limits in smartphones by proposing an efficient authentication model that enables implicit authentication without requiring additional hardware and incurring less computational cost. The research explores various wrapper feature selection strategies and classifiers to enhance authentication accuracy while considering smartphone limitations such as hardware constraints, battery life, and memory size. However, the available dataset is small; thus, it cannot support a general conclusion. In this article, a novel implicit authentication model for smartphone users is proposed to address the one-against-all classification problem in smartphone authentication. This model depends on the integration of the conditional tabular generative adversarial network (CTGAN) to generate synthetic data to address the imbalanced dataset and a new proposed feature selection technique based on the Whale Optimization Algorithm (WOA). The model was evaluated using a public dataset (RHU touch mobile keystroke dataset), and the results showed that the WOA with the random forest (RF) classifier achieved the best reduction rate compared to the Harris Hawks Optimization (HHO) algorithm. Additionally, its classification accuracy was found to be the best in mobile user authentication from their touch behavior data. WOA-RF achieved an average accuracy of 99.62 ± 0.40% with a reduction rate averaging 87.85% across ten users, demonstrating its effectiveness in smartphone authentication.

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