Phishing URL detection model based on machine learning
Subject Areas : network security
saeed ahmadi
1
(Computer Department, College of Engineering, Islamic Azad University, Tehran West Branch, Tehran, Iran)
Keywords: phishing, URL address, deep learning, channel layer, multi-head self-attention,
Abstract :
Phishing attacks have always posed significant threats to Internet security. One of the most common forms of phishing is through URLs, where attackers disguise fake URLs as valid URLs to trick users into clicking on them. Machine learning techniques have shown promise for identifying phishing URLs, but their effectiveness can vary based on the approach used. Objectives: The objective of this research is to propose two machine learning methods, "Convolutional Neural Networks" (CNN) and "Multiple Self-Awareness" (MHSA), to identify phishing URLs. In addition, to evaluate and compare the effectiveness of this approach compared to other methods and models. Research method: A dataset of URLs was collected and labeled as phishing or legitimate. The performance of several models using different machine learning methods, including CNN and MHSA, was evaluated to classify these URLs using different criteria, such as accuracy, precision, recall and F1 score. Results: The results show that the combination of CNN and MHSA models performs better than other individual models and reaches 98.3% accuracy. which provides a significant improvement in identifying phishing URLs compared to existing modern methods. Conclusion: The combination of CNN and MHSA is an effective approach to detect phishing URLs. This method performs better than existing modern methods and provides a more accurate and reliable method for detecting phishing URLs. The results of this study show the potential of using hybrid methods in improving the accuracy and reliability of phishing URL detection methods based on machine learning.
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