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DOI: 10.14569/IJACSA.2025.0160223
PDF

Enhanced Cyber Threat Detection System Leveraging Machine Learning Using Data Augmentation

Author 1: Umar Iftikhar
Author 2: Syed Abbas Ali

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 2, 2025.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: In the modern era of cyber security, cyber-attacks are continuously evolving in terms of complexity and frequency. In this context, organizations need to enhance Network Intrusion Detection Systems (NIDS) for anomaly detection. Although the existing Machine Learning models are in place to cater to the situations but new challenges emerge rapidly which affects the performance and efficiency of existing models specifically the unreachability of large datasets and unorganized data. This results in degraded efficiency for the identification of complex attacks. In this paper, data augmentation has been done of NSL-KDD which is a standard dataset for Intrusion Detection Systems (IDS) specifically for IoT-based devices. The improvement in performance and efficiency of NIDS has been performed by training the augmented dataset using the K-Nearest Neighbor (KNN) ML model.

Keywords: Anomaly detection; cyber threat intelligence; generative adversarial networks; data augmentation; Wasserstein GAN with gradient penalty

Umar Iftikhar and Syed Abbas Ali, “Enhanced Cyber Threat Detection System Leveraging Machine Learning Using Data Augmentation” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160223

@article{Iftikhar2025,
title = {Enhanced Cyber Threat Detection System Leveraging Machine Learning Using Data Augmentation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160223},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160223},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Umar Iftikhar and Syed Abbas Ali}
}



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.

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