Abstract

Every day the cyberattacks increase and they use different strategies. One of the most common cyberattacks is Phishing where the attacker collects sensitive and confidential information by pretending as a trusted party. Different traditional strategies introduced for anti-phishing such as backlisted, heuristic search, and visual similarity. Most of these traditional methods have a high false rate and they take a long time to detect the phishing website. New modes introduced using machine learning techniques which improve the detection’s accuracy. Machine learning techniques require a huge amount of data called features that are collected from different websites. These collected features are classified into four categories. This paper introduces a novel detection model by utilizing features’ selection to pick up the highly correlated features with the class label. The phase of features’ selection employs independent significance features library from MATLAB and heat-map from Python to find the highly correlated features. Then, the proposed model uses an adaptive boosting approach which consists of multiple classifiers to increase the model’s accuracy. The proposed model produces an extremely high predictive accuracy of approximately 99%.

Key words: Adaptive Boost Feature selection Correlation-based feature