Develop a Hybrid Classification using an Ensemble Model for Phishing Website Detection
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Abstract
Solutions to threats posed by technical and social vulnerabilities must be found to secure the web interface. Social engineering attacks frequently use phishing as one of their vectors. The importance is promptly detecting phishing attacks has increased. The classifier model was constructed using publicly accessible data from trustworthy and phishing websites. A variety of methods were used to extract relevant features to build the model. Before a user experiences any harm, Machine Learning algorithms can reliably identify phishing attacks. To identify phishing attacks on the website, this study presents a novel ensemble model. In this paper, the Artificial Neural Network (ANN) and the Random Forest Classifier (RFC) are used in an ensemble method along with the Support Vector Machine (SVM). Compared to previous studies, this ensemble method more accurately and efficiently detects website phishing attacks. According to experimental findings, the proposed system detects phishing attacks 97.3% of the time.
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