Phishing Website Detection Using Machine Learning: Model Development and Django Integration
Abstract
The increasing number of phishing attacks is one of the major concerns of security researchers today. Traditional solutions for spotting phishing websites rely on signature-based methods, which cannot detect newly generated phishing websites. Thus, researchers are developing machine learning-based systems capable of detecting and classifying phishing websites with high accuracy, given a vast and diverse set of data.
After several steps which requires adequate preparation of the dataset for the model development, the prepared dataset is used to train the logistic regression (LR), k-nearest neighbor (KNN) and artificial neural network (ANN) model. This research is concluded by integrating the best performing model in terms of the documented measuring metrics into the Django application. Research has proved that the integration of machine-learning model into the web application is lacking. Researchers only stop at the model performance without proper integration into the end-user consumption. Apart from the comparison of the proposed model with previous researchers work, this research will also contribute by detailing the steps required to integrate the proposed model for end-user consumption.
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