Legitimate - Fraudulent URL Detection Using Machine Learning

Abstract— With a rise in internet usage, this has brought many cyber threats; malicious URLs stand out. Its detection is highly important to the protection of users as well as securing cybersecurity. A new method will be introduced which identifies fraudulent URLs using Gated Recurrent Units, which belongs to a group of specialized forms of Recurrent Neural Network called RNNs. The high detection accuracy of this model is achieved through features derived from URL structure, domain information, and page content. GRUs differ from traditional approaches since they excel at sequential data processing. Meanwhile, to prove the practical usages of the model, a real-time detection system is also implemented. The results of this study emphasize the robustness of GRUs in countering dynamic cyber threats, paving the way for future advancements in intelligent security systems. The surge in fraudulent URLs has emerged as a critical challenge in cybersecurity, enabling phishing attacks, malware distribution, and data breaches. Traditional detection systems, such as blacklists and heuristic methods, often fail to address the dynamic and ever-evolving nature of these threats. This research introduces an innovative approach to fraudulent URL detection using Gated Recurrent Units (GRUs), a type of Recurrent Neural Network (RNN) optimized for sequential data analysis. By leveraging lexical, domain-based, and content-based features, the proposed system achieves superior accuracy and robustness. Furthermore, the implementation of this model in a real-time application highlights its practical utility in enhancing cybersecurity frameworks. Results demonstrate the system’s effectiveness in identifying fraudulent URLs with minimal false positives, paving the way for scalable and adaptive solutions to combat emerging cyber threats Keywords- Fake URLs, GRU, Machine Learning, Cybersecurity, URL Detection, Neural Networks, Online Threat Mitigation.