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@article{178047,
        author = {Chintha sarath kumar and Dr.S.Latha},
        title = {Identifying phishing domains using ai/ml},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2400-2404},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178047},
        abstract = {The rapid advancement of internet and cloud technologies has significantly increased online transactions and e-commerce activities. However, With the rapid expansion of the digital world, cyber threats—particularly phishing attacks—have also seen a significant rise. Phishing schemes trick individuals into disclosing sensitive information by imitating legitimate websites, often closely copying the look and URL patterns of genuine platforms, making them hard to detect through standard security measures. Traditional defense methods like blacklists and heuristic techniques have shown clear shortcomings when it comes to addressing these sophisticated threats. The decentralized and often anonymous nature of the internet only worsens the challenge, giving cybercriminals more room to operate. Research has shown that many existing phishing detection tools are struggling to keep up with the increasingly advanced methods used by attackers. In response to these challenges, this study investigates the use of machine learning for detecting phishing websites. Five different classification algorithms were tested—Logistic Regression, Random Forest, Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN). Among them, Logistic Regression delivered the best results, achieving a detection accuracy of 98.5%, underlining its potential in strengthening online security against phishing threats.},
        keywords = {Phishing Detection, NLP, Blacklists, Heuristic Approaches, URL-based Features, Lexical Features, Network-related Features, Random Forest, XG Boost, Real-time Adaptability, False Positives Reduction , Tokenization},
        month = {May},
        }