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@article{177074,
        author = {Paiavula Ankith and A.Hemanth and B.Suresh and K.Karthik and P.Sathish},
        title = {Modeling and Predicting Cyber Hacking Breaches},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3670-3674},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177074},
        abstract = {The paper offers a practical method for detecting and classifying malicious URLs using a machine learning model that utilizes the Support Vector Machine (SVM) algorithm. The system determines whether URLs are malicious or safe by analyzing a dataset that contains known malicious indicators associated with different types of cyberattacks, including phishing, man-in-the-middle, and SQL injection. Real-world flag data is used to support the research and it was tested on a diverse set of URLs. By storing and analyzing the results, statistical insights are gained, which allows for visualization of attack distributions. By offering an automated, data-driven framework, the study helps cybersecurity research by identifying and mitigating web-based threats.},
        keywords = {},
        month = {May},
        }