ScholarWorks@성균관대학교

초록

Illicit web ecosystems, encompassing phishing, illegal online gambling, scam platforms, and malicious advertising, have rapidly expanded in scale and complexity, creating severe social, financial, and cybersecurity risks. Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic, multilingual, and adversarially manipulated threats, resulting in increasing demand for Artificial Intelligence (AI)-based solutions. This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments. It surveys detection models across multiple modalities, including text-based analysis of Uniform Resource Locator (URL) and HyperText Markup Language (HTML), vision-based recognition of webpage layouts and logos, graph-based modeling of domain and infrastructure relationships, and sequence modeling using transformer architectures. In addition, the paper examines system architectures, data collection and labeling pipelines, real-time detection frameworks, and widely used benchmark datasets, while also discussing their inherent limitations related to imbalance, representativeness, and reproducibility. The review highlights critical challenges such as evasion strategies, cross-lingual detection barriers, deployment latency, and explainability gaps. Furthermore, it identifies emerging research directions, including the use of Generative Adversarial Network (GAN) for threat simulation, few-shot and self-supervised learning for data-scarce environments, Explainable Artificial Intelligence (XAI) for transparency, and predictive AI for proactive threat forecasting. By integrating technical, legal, and societal perspectives, this survey offers a structured foundation for researchers and practitioners to design resilient, adaptive, and trustworthy AI-based defense systems against illicit web threats.

키워드