TY  - EJOU
AU  - Hwang, Jaeho 
AU  - Min, Moohong 

TI  - Survey of AI-Based Threat Detection for Illicit Web Ecosystems: Models, Modalities, and Emerging Trends
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 3
SN  - 1526-1506

AB  - 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.
KW  - Artificial intelligence (AI); cybersecurity; threat detection; machine learning (ML); deep learning (DL); phishing; illegal online gambling; illicit websites; resilient detection; explainable artificial intelligence (XAI)

DO  - 10.32604/cmes.2026.078940