Intelligent Ensemble Learning Approach for Phishing Website Detection Based on Weighted Soft Voting

The continuous development of network technologies plays a major role in increasing the utilization of these technologies in many aspects of our lives, including e-commerce, electronic banking, social media, e-health, and e-learning. In recent times, phishing websites have emerged as a major cybersecurity threat. Phishing websites are fake web pages that are created by hackers to mimic the web pages of real websites to deceive people and steal their private information, such as account usernames and passwords. Accurate detection of phishing websites is a challenging problem because it depends on several dynamic factors. Ensemble methods are considered the state-of-the-art solution for many classification tasks. Ensemble learning combines the predictions of several separate classifiers to obtain a higher performance than a single classifier. This paper proposes an intelligent ensemble learning approach for phishing website detection based on weighted soft voting to enhance the detection of phishing websites. First, a base classifier consisting of four heterogeneous machine-learning algorithms was utilized to classify the websites as phishing or legitimate websites. Second, a novel weighted soft voting method based on Kappa statistics was employed to assign greater weights of influence to stronger base learners and lower weights of influence to weaker base learners, and then integrate the results of each classifier based on the soft weighted voting to differentiate between phishing websites and legitimate websites. The experiments were conducted using the publicly available phishing website dataset from the UCI Machine Learning Repository, which consists of 4898 phishing websites and 6157 legitimate websites. The experimental results showed that the suggested intelligent approach for phishing website detection outperformed the base classifiers and soft voting method and achieved the highest accuracy of 95% and an Area Under the Curve (AUC) of 98.8%.