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{179662,
        author = {Gayathri D and Arthi M and MuthuRoja P},
        title = {Cyber Threat Detection System using Machine Learning and Real-time Alerting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7836-7840},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179662},
        abstract = {This project aims to enhance cybersecurity by accurately detecting and responding to three critical types of cyber attacks: brute force, Distributed Denial-of-Service (DDoS), and man-in-the-middle (MITM). The system leverages advanced detection algorithms and real-time monitoring techniques to identify threats as they occur, significantly reducing response time. By narrowing the detection scope from a broader set of 14 attacks to three high-impact categories, the system improves precision and reduces false positives. The expected outcome is a more reliable and efficient threat detection framework that outperforms traditional systems in both accuracy and alert speed.},
        keywords = {Brute Force Attack, Cybersecurity, DDoS Detection, MITM, Real-Time Alert, Threat Detection System},
        month = {May},
        }