Design and Development of a Machine Learning-Based System for Detection of Phishing Websites
DOI:
https://doi.org/10.64882/ijrt.v14.i3.1912Keywords:
phishing websites, machine learning, random forest, website classification, grouped holdoutAbstract
Based on the UCI Phishing Websites dataset, a replicable machine learning system was created to categorise phishing websites. It consists of 11,055 tagged examples and 30 pre-computed website characteristics. Data examination showed 5,785 distinct feature vectors and a lot of recurring patterns. Then, a grouped holdout was used to keep the same feature vectors together while splitting. The experiment contrasted a decision tree, a random forest, and a majority class baseline using a similar training and testing process. The random forest achieved 96.13% accuracy, 95.51% phishing precision, 96.00% phishing recall, 95.75% phishing F1 and 0.9939 ROC AUC on 2,144 heldout data. The confusion matrix yielded 935 properly identified phishing records, 39 missed phishing records, 44 false warnings, and 1,126 correctly categorised real data. Additional threshold, repeated-split and feature-removal analysis further illuminate the stability and limitations of this finding. The provided Python implementation generates the tables, execution outputs and graphs. The programme takes the 30 characteristics that are provided; to categorise a raw URL, an extra matching extraction step and provide external validation would be required.
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