Phishing Site Detection from a Web Developer's Perspective



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The Internet has enabled unprecedented communication and new technologies. Concomitantly, it has brought the bane of phishing and exacerbated vulnerabilities. In this thesis, we propose a model to detect phishing webpages from a web developer's perspective. From this standpoint, we design 120 novel features based on content from a webpage, four time-based and two search-based novel features, plus we use 34 other content-based and 11 heuristic features to optimize the model. Moreover, we select Random Committee (Base learner: Random Tree) for our framework since it has the best performance after comparing with six other algorithms: Hellinger Distance Decision Tree, Support Vector Machine (SVM), Logistic Regression, J48, Naive Bayes, and Random Forest. In real-time experiments, the model achieved 99.4% precision and 98.3% Matthews correlation coefficient (MCC) with 0.1% false positive rate in 5-fold crossvalidation using the realistic scenario of an unbalanced dataset.



Random Committee, Machine learning, Phishing site, Phishing