Comprehensive Guide to Malicious Website Anti‑Fraud: Detection, Operation, and Modeling

Introduction: Anti‑fraud is a silent war for security professionals; with the rise of black‑industry activities, protecting users requires thorough understanding and mitigation of malicious websites.

Understanding malicious websites: They are categorized into fraud, gambling, pornographic, and other types such as phishing or piracy. Development often follows a complete industrial chain, using purchased domains, source code, or custom builds, resulting in many similar yet distinct domains.

Operation and revenue: Operators attract users via SMS, ads, or multi‑level agents, and evade controls using short‑link jumps, embedded displays, or red‑code. Revenue is mainly ad‑based, with low cost and high profit.

Detection of malicious sites: Abnormal detection examines traffic, channel, content, and relationship dimensions. Traffic anomalies show sudden spikes or drops; channel anomalies reveal unusual distribution; content anomalies include large redirects, multi‑protocol differences, or geo‑restrictions; relationship anomalies use link and jump analysis.

Counter‑measures: Models include structural (fingerprint) detection, text models (sensitive‑word rules, clustering, classification), image models (classification, explanation, similarity), complex network models (website graphs, node prediction, community detection), and multimodal models (feature, decision, and co‑training fusion).

Operation and intelligence system: Consists of stability monitoring, explosion‑prevention, and user‑feedback operations, as well as intelligence gathering on black‑industry resources, intrusion‑related sites, and service‑provider chains.

Q&A highlights: Graph neural networks can operate on website graphs with sampling; black‑SEO detection relies on text cues; large models assist image‑based fraud detection; embedding attacks and dynamic node changes are addressed through sampling and periodic re‑evaluation.