Cybersecurity Threat Detection Using Machine Learning

  • Unique Paper ID: 198372
  • Volume: 12
  • Issue: 11
  • PageNo: 9118-9119
  • Abstract:
  • This paper proposes ThreatAI, a Machine Learning-based cybersecurity system for real-time intrusion detection. Using Random Forest and XGBoost, the system classifies network traffic and detects malicious activities with high accuracy.

Copyright & License

Copyright © 2026 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{198372,
        author = {Ashwini and Jayalakshmi T S},
        title = {Cybersecurity Threat Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9118-9119},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198372},
        abstract = {This paper proposes ThreatAI, a Machine Learning-based cybersecurity system for real-time intrusion detection. Using Random Forest and XGBoost, the system classifies network traffic and detects malicious activities with high accuracy.},
        keywords = {Cybersecurity, Intrusion Detection, Machine Learning, Random Forest, XGBoost, ThreatAI, Network Security},
        month = {April},
        }

Cite This Article

Ashwini, , & S, J. T. (2026). Cybersecurity Threat Detection Using Machine Learning. International Journal of Innovative Research in Technology (IJIRT), 12(11), 9118–9119.

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