Evaluation of Phishing Attacks Targeting Local Systems Using an Attribute-Based Dataset and Machine Learning Methods

Phishing attacks are a form of social engineering (tricking people into revealing secrets by pretending to be trustworthy) that trick users into visiting fake websites that look like real ones to steal sensitive information. Researchers created a new dataset with 31 attributes (measurable characteristics) derived from URLs and similarity features, then tested multiple machine learning algorithms (computer programs that learn patterns from data) on it to detect these attacks. The Logistic Regression method achieved 96.40% accuracy at detecting phishing, showing that this approach works well for protecting local systems in real-world situations.