“Think Before You Click”—Malicious URL Detection in Cybersecurity: A Systematic Review and Research Roadmap

Introduction: Thanks to the world wide web (www) revolutions and COVID-19 pandemic, the dependency on digital platforms has tremendously increased and making them essential for day-to-day information access. Although, technological advancements boosted digital platform usage, but also increased user’s vulnerability to cyberattacks. Background: For instance, In India, the reported cyber crime complaints increased by 15.3% in Quarter-2 of 2022, totaling 237,658 complaints. Further, Phishing attacks, are responsible for over 90% of data breaches, which are the most common and severe cyber threats. Despite the diversity of cyber-threats, URLs serve as the primary gateway in most attacks, making their detection crucial for protecting users. Methodology: This article provides a comprehensive literature review on malicious URL detection techniques, including Listing, Heuristics, Machine Learning, Feature Engineering and Emerging Methods, by highlighting limitations, feature types, and datasets and thereby presents the broader perspective compared to contemporary single-methodology-based review studies. Further, this article follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model for a thorough, transparent review process. Results: Based on the research study, this article highlights unexplored research challenges associated with each category of detection techniques, feature types, which play a crucial role in effective malicious URL detection. It also emphasizes on Theoretical, Managerial implications of this study, Real-world deployment constraints, Research Roadmap and thereby encourages the future researchers to address these challenges and develop innovative solutions.