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.
@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},
}
Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.
Join NowNational Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024
Submit inquiry