An Intelligent Approach to Phishing Detection Using Machine Learning

Keerthi C

An Intelligent Approach to Phishing Detection Using Machine Learning

Author(s)	Keerthi C
Country	India
Abstract	Phishing is one of the simplest yet most widely used techniques by cyber attackers to steal sensitive information from unsuspecting users. Attackers create fake websites that closely resemble legitimate ones in order to trick individuals into revealing confidential data such as usernames, passwords, and banking details. As phishing attacks continue to grow in number and complexity, it has become essential to develop accurate and automated systems for their detection. This research proposes a machine learning-based approach to identify and classify phishing and legitimate URLs. The method involves extracting relevant features from URLs and analyzing them using different machine learning algorithms. In this study, models such as Extreme Gradient Boosting, Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine are applied and compared to determine their effectiveness in phishing detection. To evaluate the proposed system, two datasets are utilized, namely Phish Tank and a dataset from the UCI repository. Various techniques, including K-fold cross-validation, feature selection, and hyper parameter optimization, are employed to improve model performance and reliability. The evaluation is carried out using standard metrics such as precision, recall, F1-score, and the Receiver Operating Characteristic curve. The experimental results indicate that the Random Forest model performs better than the other algorithms, achieving high accuracy on both datasets. It also provides strong values for precision, recall, and F1-score, along with a high ROC score, demonstrating its capability to effectively distinguish phishing URLs from legitimate ones. When compared with existing approaches, the proposed method shows improved performance, making it a useful solution for enhancing phishing detection systems.
Keywords	Phishing Detection, Machine Learning, Cyber security, URL Classification, Random Forest, SVM, XGBoost
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-25

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An Intelligent Approach to Phishing Detection Using Machine Learning

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