Recognition of Malicious URLs Using Machine Learning
Sweety Garg, Ms. Sayed Shifa Mohd Imran · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024
The surge in online activities has heightened the risk of cyber-attacks, with phishing emerging as a notably widespread threat. Conventional phishing detection methods, such as blacklists and heuristic analysis, fall short when it comes to identifying novel and complex phishing schemes. To tackle this issue, we propose a machine learning-based solution for detecting phishing websites. Our model utilizes a combination of features—such as URL structure, domain attributes, and content characteristics—to categorize websites as either phishing or legitimate. The model's outstanding overall accuracy of 96.9% underscores the need to evaluate both precision and recall when determining the effectiveness of machine learning models. The results of this study are significant for developing robust URL classification models and advancing the field of cybersecurity. Future research could aim to improve the model's performance by integrating more features, developing real-time phishing detection systems, and exploring new attributes that can be derived from URLs. Keywords: Phishing, URL, Machine Learning, Classification, Detection