Phishing Website Detection through Ensemble Machine Learning Techniques

Phishing attacks have become increasingly sophisticated, posing a significant threat to individuals and organizations. The ability to detect phishing websites is crucial for mitigating potential risks and safeguarding sensitive information. Traditional methods of detecting phishing websites often struggle to keep pace with the evolving tactics employed by cybercriminals. As a result, there is a pressing need for innovative and adaptive solutions to identify and combat this pervasive threat. This paper proposed an advanced phishing detection system by leveraging the power of machine learning ensemble algorithms. A dataset from Kaggle was collected. Initially, four ML classifiers namely Random /forest, Extra tree classifier, Gradient boosting classifier and logistic regression classifier applied for phishing website detection. Later ensemble of these four ML algorithms with different ensemble method is develop for phishing website detection. In ensemble, two types of ensemblesapproach namely stacking ensemble and voting ensemble applied. Experimental results showcased the potential of this ensemble approach to improve accuracy and adaptability in the prediction of phishing websites.