Machine Learning-Based Phishing Website Identification System
Keywords:
Decision tree, Machine learning, Phishing website detection, Random forest, Real-time detection, Support vector machine, URL feature extractionAbstract
The rapid growth of online services has led to a significant increase in phishing attacks, making phishing website detection a critical area of cybersecurity research. Machine learning techniques have emerged as an effective solution by enabling faster and more accurate identification of fraudulent websites. This literature survey reviews four recent studies that apply different machine learning approaches, including Random Forest, Support Vector Machine (SVM), Decision Tree, hybrid models, and AI-driven techniques, for phishing website detection. The reviewed works examine various URL-based, domain-related, and webpage content features to distinguish malicious websites from legitimate ones while addressing issues such as changing phishing strategies, feature selection, and real-time detection. A comparative analysis of these studies highlights their methodologies, performance, advantages, and limitations, helping to identify current research trends and existing challenges. Based on the findings, the survey indicates that integrating effective feature engineering with the Random Forest algorithm can improve the accuracy and reliability of real-time phishing detection systems. The study also provides useful insights for designing practical, secure, and user-friendly solutions that strengthen cybersecurity and promote safer web browsing.
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