PhiBoost : a novel phishing detection model using adaptive boosting approach

Abstract EN

Every day, cyberattacks increase and 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 have been introduced for anti-phishing, such as blacklisted, heuristic search and visual similarity.

Most of these traditional methods have a high false rate and take a long time to detect the phishing website.

New modes have been 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%.