ENSEMBLE MODEL FOR THE DETECTION OF PHISHING URLs
Abstract
Context: The rise in internet usage has led to a surge in online security risks, with phishing attacks being a major cause for concern. These attacks frequently involve the use of misleading web addresses to deceive users into revealing sensitive information. Detecting such harmful URLs is vital for safeguarding user data and online privacy. Objective: In this work, we present a combined model for identifying phishing URLs, harnessing the capabilities of machine learning algorithms, particularly Naive Bayes and Random Forest. Method: Our approach began by collecting a diverse dataset of web addresses, encompassing both legitimate and phishing URLs. We then performed feature extraction to represent these web addresses as numerical vectors, capturing their lexical features. These numerical features serve as input for training two well-known machine learning models: Naive Bayes and Random Forest. The Naive Bayes classifier, rooted in probabilistic principles, offers simplicity and efficiency in classification tasks. It constructs models based on the conditional probabilities of features given the class label, enabling it to make informed predictions. On the other hand, Random Forest, an ensemble learning technique, combines predictions from numerous decision trees to enhance accuracy and robustness. To make a final prediction, we employed a voting classifier that incorporates the predictions of Naive Bayes and Random Forest. Results: The result of our experiment indicated the efficacy of the voting classifier in identifying phishing URLs, as the model achieved an accuracy rate of 94.10%. We evaluated the performance of the voting classifier using various metrics, such as accuracy, precision, recall, and F1-score, revealing the ability to effectively differentiate between legitimate and phishing web addresses. Conclusions: This work contributes to the advancement of phishing URL detection methods, providing insights into the suitability of Naive Bayes and Random Forest for this purpose. Moreover, the proposed approach can aid in the development of more robust and accurate phishing detection systems, thereby enhancing online security and protecting users from cyber threats.
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