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@article{178124,
        author = {Arya A and Bhoomika N B and Ashith and Nikhil Shastry and Thrisha V S},
        title = {An AI-Powered Integrated System for Real-Time  Cybersecurity Threat Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2459-2462},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178124},
        abstract = {The expanding size and sophistication of cyber threats have made conventional security measures insufficient. This paper describes an AI-Powered Cybersecurity Threat Detection System based on machine learning algorithms to classify and identify threats like malware, phishing, and brute-force attacks in real time. We enhanced the publically available data by using Generative Adversarial Networks (GANs) to synthesize realistic attack data and the combined dataset was then used to train the machine learning models such as Random Forest and Decision Tree classifiers to provide high detection accuracy. It is executed with Python and deployed via a Flask-based web interface, allowing users to upload network data and obtain instant threat analysis. Experimental results show high precision, recall, and overall accuracy, confirming the effectiveness of the system in early threat detection. The method drastically minimizes manual effort and response time, providing a scalable and automated solution for improving cybersecurity defenses.},
        keywords = {Artificial Intelligence, Cybersecurity, Machine Learning, Threat Detection.},
        month = {May},
        }