Abstract
The damaging effect of phishing is traumatizing as attackers or hackers execute theft of sensitive information from users subtly for inappropriate or unauthorized usage. In the light of curbing phishing, blacklisting of websites proved ineffective as the deployment of phishing websites are rampantly increasing and often short-lived. Hence, machine learning (ML) methods are seen as viable measures and used to develop deplorable models that can detect a phishing website. ML methods are fast gaining attention and acceptance in detecting phishing websites as they can cope with the dynamism of phishing websites and attackers. However, ML methods still suffer some shortcomings in terms of low detection accuracy, high false alarm rate (FAR) and induced bias of developed ML solutions. In addition, with the evolving nature of phishing attacks, there is a continuing imperative need for novel and effective ML-based methods for detecting phishing websites. This study proposed 3 meta-learner models based on Forest Penalizing Attributes (ForestPA) algorithm. ForestPA uses a weight assignment and weight increment strategy to build highly efficient decision trees by exploiting the prowess of all attributes (non-class inclusive) in a given dataset. From the experimental results, the proposed meta-learners (ForestPA-PWDM, Bagged-ForestPA-PWDM, and Adab-ForestPA-PWDM) are highly efficient with the least accuracy of 96.26%, 0.004 FAR, and 0.994 ROC value. Further, with the superiority of the proposed models over other existing methods, we recommend the development and adoption of meta-learners based on ForestPA for phishing website detection and other cybersecurity attacks.