Phish webpage classification using hybrid algorithm of machine learning and statistical induction ratios

Although the conventional machine learning-based anti-phishing techniques outperform their competitors in phishing detection, they are still targeted by zero-hour phish webpages due to their constraints of phishing induction. Therefore, phishing induction must be boosted up with the extraction of new features, the selection of robust subsets of decisive features, the active learning of classifiers on a big webpage stream. In this paper, we propose a hybrid feature-based classification algorithm (HFBC) for decisive phish webpage classification. HFBC hybridises two statistical criteria optimised feature occurrence (OFC) and phishing induction ratio (PIR) with the induction settings of the most salient machine learning algorithms, Naïve bays and decision tree. Additionally, we propose two constituent algorithms of features extraction and features selection for holistic phish webpage characterisation. The superiority of our proposed approach is justified and proven throughout chronological, real-time, and comparative analyses against existing machines learning-based anti-phishing techniques.