PhishDetect: A ranking-based classifier integration approach for improving phishing website detection

Phishing is a fraudulent activity that includes tricking folks into disclosing personal information by impersonating a legitimate individual or organization. Nowadays, phishing attacks are increasing due to the widespread availability of Internet access, leading more individuals to use online platforms for various services like banking, shopping, etc. Cybercriminals exploit this shift using various tricks to find their victims online. The cybersecurity experts and professionals are leveraging machine learning to enhance phishing detection rate, as conventional methods are becoming less effective. The conventional machine learning and ensemble learning approaches often result in high false positive and false negative rates. Thus, it is essential to design and develop more reliable solutions for identifying phishing webpages. The primary contribution of the paper is enhancing phishing detection accuracy by combining base classifiers using ranking schemes derived from their prediction errors. The effectiveness of the proposed approach is evaluated using a benchmark dataset. The results reveal that the proposed approach outperforms traditional machine learning and ensemble learning methods in phishing detection. The proposed approach provides the weighted F-measure of 0.984 as compared to the stacking of all classifiers and top three classifiers selected using ranking strategies which achieve the weighted F-measure of 0.970 and 0.974, respectively. Further, to evaluate the validity and generalization capability of the proposed approach, experiments are conducted using an additional standard benchmark dataset.