A novel approach to intrusion detection using zero-shot learning hybrid partial labels

  • Syed Atir Raza School of Information Technology, Minhaj University Lahore, 54000 Pakistan
  • Mehwish Shaikh Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro Pakistan
  • Raybal Akhtar School of Systems and Technology, University of Management and Technology, Lahore, 54000 Pakistan
  • Aqsa Anwar School of Software Engineering, Minhaj University Lahore, 54000 Pakistan

Abstract

Computer networks have become the backbone of our interconnected world in today's technologically driven landscape. Unauthorized access or malicious activity carried out by threat actors to acquire control of network resources, exploit vulnerabilities, or undermine system integrity are examples of network intrusion. ZSL(Zero-Shot Learning) is a machine learning paradigm that addresses the problem of detecting and categorizing objects or concepts that were not present in the training data. . Traditional supervised learning algorithms for intrusion detection frequently struggle with insufficient labeled data and may struggle to adapt to unexpected assault patterns. In this article We have proposed a unique zero-shot learning hybrid partial label model suited to a large image-based network intrusion dataset to overcome these difficulties. The core contribution of this study is the creation and successful implementation of a novel zero-shot learning hybrid partial label model for network intrusion detection, which has a remarkable accuracy of 99.12%. The suggested system lays the groundwork for future study into other feature selection techniques and the performance of other machine learning classifiers on larger datasets. Such research can advance the state-of-the-art in intrusion detection and improve our ability to detect and prevent the network attacks. We hope that our research will spur additional research and innovation in this critical area of cybersecurity.

Published
Jan 1, 2024
How to Cite
RAZA, Syed Atir et al. A novel approach to intrusion detection using zero-shot learning hybrid partial labels. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 43, n. 1, p. 182-191, jan. 2024. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2945>. Date accessed: 27 feb. 2024. doi: http://dx.doi.org/10.22581/muet1982.2401.2945.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License