Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning algorithms

  • Unique Paper ID: 161987
  • Volume: 10
  • Issue: 7
  • PageNo: 154-160
  • Abstract:
  • This project focuses on advanced machine learning for detecting intrusions in imbalanced network traffic. Despite challenges posed by data imbalance, the study employs cutting-edge algorithms and diverse preprocessing to glean insights from normal and malicious patterns. Findings highlight machine learning's potential in managing imbalanced network traffic, emphasizing tailored preprocessing and algorithm selection. Ultimately, the project advances intrusion detection by showcasing machine learning's role in enhancing security through swift threat identification and mitigation. In conclusion, this research underscores the pivotal role of machine learning in addressing imbalanced network scenarios, paving the way for a safer digital landscape.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{161987,
        author = {S Sai Varun and D. Sai Sumanth and G. Sai Vardhan and P. Sai Vamshi and S. Sai Varshith and T. Sai Sushanth},
        title = {Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {7},
        pages = {154-160},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161987},
        abstract = {This project focuses on advanced machine learning for detecting intrusions in imbalanced network traffic. Despite challenges posed by data imbalance, the study employs cutting-edge algorithms and diverse preprocessing to glean insights from normal and malicious patterns. Findings highlight machine learning's potential in managing imbalanced network traffic, emphasizing tailored preprocessing and algorithm selection. Ultimately, the project advances intrusion detection by showcasing machine learning's role in enhancing security through swift threat identification and mitigation. In conclusion, this research underscores the pivotal role of machine learning in addressing imbalanced network scenarios, paving the way for a safer digital landscape.},
        keywords = {},
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 7
  • PageNo: 154-160

Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning algorithms

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