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@article{193733,
        author = {Mrs.Shaik Shameen Taz and Shaik Mehtaj and Laisetty Prem Kumar and Danduboyina Rajeev and Parapatla Sai Kumar},
        title = {MACHINE LEARNING TECHNIQUES FOR IDENTIFYING PHISHING WEBSITES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2396-2402},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193733},
        abstract = {Phishing attacks remain one of the most prevalent cybersecurity threats, where attackers create fake websites to steal sensitive user information such as login credentials and financial data. Traditional phishing detection methods rely on manual inspection or static feature-based machine learning models, which often fail to detect new or evolving phishing patterns. This project proposes a robust machine learning-based phishing website detection system that leverages an enhanced feature set, including URL characteristics, domain information (such as age, SSL certificate, and registration details), and optional webpage content features. The system employs an ensemble of Random Forest and XGBoost classifiers, with Logistic Regression as a baseline, to achieve high detection accuracy. Additionally, explainable AI techniques (SHAP/LIME) are integrated to provide transparency and interpretability in the detection process. Designed for real-time URL analysis, the system aims to reduce false positives, improve adaptability to new phishing strategies, and provide trustworthy results.},
        keywords = {Phishing Identification, Machine Learning, Cyber Security, XGBoost, Random Forest, Logistic Regression, Decision Tree, URL Analysis, Classification, Feature Extraction.},
        month = {March},
        }