# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 1 Introduction  
The proliferation of internet usage in our daily lives has introduced a surge in cyber threats, with malicious uniform resource locators being a prevalent vehicle for cybercrimes like phishing attacks and malware distribution [1, 2]. A malicious URL has been designed to disseminate viruses, ransomware, spyware, malware, phishing, and other threats. By clicking on an infected URL, a user may download malware that could negatively influence the computer or be tricked into giving personal information to a fraudulent website that could result in significant losses. These threats need to be recognized and dealt with quickly and effectively. Traditionally, the blacklist method is used, which matches keywords with known malicious domain names kept in a repository used for detection. This approach has the benefit of low false positive rates concerning previously identified malicious URLs, and it is quick and simple to use. Nevertheless, this technique is unable to identify recentlycreated malicious URLs. To address these challenges, contemporary researchers employ classical machine learning methodologies [3]. The increasing digitization of daily activities including banking, social networking, e-commerce, and business operations is accompanied by a corresponding escalation in cybercrime risks. Malicious actors routinely weaponize URLs as attack vectors, employing them to deceive users and facilitate unauthorized system access or data breaches. This growing threat landscape underscores the critical importance of robust URL security measures. While established protocols and legal frameworks provide some protection for client-server communications, determined adversaries continue to exploit vulnerabilities. These threats manifest in various forms, including phishing, spam, and adhere, collectively categorized as malware. Annually, malicious URL schemes defraud unsuspecting users worldwide, resulting in financial losses exceeding billions of dollars. In response, the cybersecurity community has implemented blacklisting services that catalog known malicious URLs [4; 5]. However, these reactive measures primarily address previously identified threats, leaving systems vulnerable to novel attack vectors. Detecting and mitigating malicious URLs is a critical aspect of maintaining online security. Traditional methods for identifying malicious URLs such as rule-based or signature-based techniques have been widely used to detect known threats. However, these approaches often fall short in the face of the rapidly evolving tactics employed by cybercriminals. As new and uniquely crafted malicious URLs continue to emerge, conventional detection methods struggle to keep pace, resulting in reduced effectiveness. This highlights a critical need for more sophisticated and adaptive detection approaches that can respond to the dynamic nature of cyber threats. Advanced, robust techniques are essential for accurately identifying and mitigating these threats in real-time [6]. While existing machine learning and deep learning models have shown promise in detecting malicious URLs, they often face several limitations. Traditional machine learning approaches heavily depend on manually engineered features, which are prone to obsolescence as cybercriminals continually evolve their tactics. Deep learning models, such as those relying solely on character-level or word-level embedding, may struggle to capture both fine-grained structures and contextual relationships in URLs, leading to reduced generalizability and susceptibility to attacks like typo squatting and character substitution [7]. To address these shortcomings, we propose a hybrid deep learning framework, Char2B, that integrates BERT embedding for capturing contextual, sub-word-level relationships and CharBiGRU embedding for capturing fine-grained character-level features. By fusing these, with embedding using a Conv1D layer, the model effectively combines local and global URL characteristics.