AI-Driven Cybersecurity Framework for Phishing Website Detection Using Optimized Machine Learning Models

Abstract

Phishing attacks remain one of the most critical cybersecurity threats, exploiting users through fraudulent websites to obtain sensitive information such as credentials and financial data. Traditional rule-based detection systems often lack adaptability to evolving attack strategies. This study proposes an AI-driven cybersecurity framework for phishing website detection using supervised machine learning models. The UCI Phishing Website Dataset consisting of 1353 instances and 10 security-related attributes was used for experimentation. Three classifiers—Logistic Regression, Support Vector Machine (SVM), and Random Forest—were implemented and comparatively evaluated. Hyperparameter optimization using GridSearchCV with 5-fold cross-validation was performed to enhance predictive performance. The dataset was split into 70% training and 30% testing subsets. Performance evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results show that the optimized Random Forest model achieved approximately 91% accuracy, outperforming Logistic Regression and SVM models. Feature importance analysis highlights that attributes such as SFH and SSLfinal_State significantly influence classification outcomes. The findings demonstrate that ensemble-based AI techniques strengthen phishing detection systems and provide scalable, intelligent cybersecurity defense mechanisms.

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Identifiers

DOI

10.22214/ijraset.2026.77759

Journal

International Journal for Research in Applied Science and Engineering Technology

Year

2026