An Intelligent Machine Learning-based System for Detection of Phishing Domains Using AI/ML
Keywords:
Cybersecurity, Domain features, Machine learning, Phishing detection, Random forest, URL analysisAbstract
Phishing attacks continue to pose a serious challenge to cybersecurity by exploiting deceptive domain names that closely resemble legitimate websites. Phishing detection techniques mainly depend on static features that are chosen using traditional feature selection or ranking techniques. Existing detection techniques, including blacklist-based and rule-driven systems, are often ineffective against newly created phishing domains and zero-day attacks. In this work, an intelligent machine learning-based system is developed to identify phishing domains by analysing URL and domain-level characteristics. The proposed approach focuses on extracting lexical, structural, and statistical features that capture common patterns observed in malicious domains. Machine learning classifiers, including Random Forest and Support Vector Machine, are trained and evaluated using a publicly available dataset containing both phishing and legitimate URLs. Random Forest shows strong performance with faster execution, making it suitable for real-time applications where fast and reliable decisions are essential. Experimental evaluation shows that the Random Forest model achieves higher accuracy, precision, recall, and F1-score, and better overall performance, compared to SVM. The results indicate that domain-level feature analysis combined with machine learning provides an effective and scalable solution for real-time phishing domain detection. The findings confirm that feature-driven intelligent systems can successfully detect phishing domains that imitate the look and feel of genuine websites. The proposed system offers a scalable, accurate, and real-time solution for phishing domain detection and contributes to strengthening cybersecurity defences against evolving phishing strategies. The purpose is to give users a reliable tool that warns them instantly about suspicious websites, reduces the risk of financial and data loss, and strengthens overall trust in online activities.
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