Survey of AI-Based Threat Detection for Illicit Web Ecosystems: Models, Modalities, and Emerging Trends

Jaeho Hwang; Moohong Min

doi:10.32604/cmes.2026.078940

January 1, 2026Computer Modeling in Engineering & SciencesOpen Access

Survey of AI-Based Threat Detection for Illicit Web Ecosystems: Models, Modalities, and Emerging Trends

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Key Points

The study aims to analyze various AI-based models used in detecting threats within illicit web ecosystems.
Conducted a survey of existing AI models for threat detection.
Examined modalities used in addressing phishing, scam platforms, and malicious advertising.
Identified emerging trends in the field of cybersecurity regarding illicit online activities.
Highlighted the rapid growth of illicit web activities.
Found that AI models significantly improve detection capabilities.
Indicated a need for enhanced security measures in response to evolving threats.

Implication

Survey examines AI models for detecting threats in illicit online environments, indicating advanced security needs.

Abstract

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. Tra... | Find, read and cite all the research you need on Tech Science Press

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Cite This Study

Hwang et al. (2026) studied this question.

synapsesocial.com/papers/69cd7a2b5652765b073a71ff https://doi.org/10.32604/cmes.2026.078940

View Full Paper

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Survey of AI-Based Threat Detection for Illicit Web Ecosystems: Models, Modalities, and Emerging Trends

Key Points

Implication

Abstract

Discussion

Cite This Study

Survey of AI-Based Threat Detection for Illicit Web Ecosystems: Models, Modalities, and Emerging Trends

Key Points

Implication

Abstract

Discussion

Cite This Study

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