新注册色情赌博域名识别率达97.88%，支持真实数据全流程检测
Real-PGDN: A Two-level Classification Method for Full-Process Recognition of Newly Registered Pornographic and Gambling Domain Names
- 两级分类器融合BERT与传统算法，支持缺失特征下的精准识别
- 在150万域名上实现97.88%精确率，延迟使用域名仍保持超70%预测精度
- 构建20天连续监测的NRD2024数据集，适用于实际监管场景
网络色情与赌博持续威胁个人资产与隐私安全，亟需对新注册的色情赌博域名（PGDN）进行有效识别。现有研究或依赖理想数据追求高准确率，或采用真实数据但精度不足。本文提出Real-PGDN方法，实现真实数据的完整采集、带缺失特征的特征提取、精准分类及实际应用效果评估。所提两级分类器融合CoSENT（基于BERT）、MLP与传统算法，在1,500,000个新注册域名（覆盖6个方向）上连续20天监测数据下达到97.88%精确率。案例研究显示，该方法对注册后延迟使用的PGDN仍保持超过70%的预测精度。
原文摘要 · Abstract (English)
Online pornography and gambling have consistently posed regulatory challenges for governments, threatening both personal assets and privacy. Therefore, it is imperative to research the classification of the newly registered Pornographic and Gambling Domain Names (PGDN). However, scholarly investigation into this topic is limited. Previous efforts in PGDN classification pursue high accuracy using ideal sample data, while others employ up-to-date data from real-world scenarios but achieve lower classification accuracy. This paper introduces the Real-PGDN method, which accomplishes a complete process of timely and comprehensive real-data crawling, feature extraction with feature-missing tolerance, precise PGDN classification, and assessment of application effects in actual scenarios. Our two-level classifier, which integrates CoSENT (BERT-based), Multilayer Perceptron (MLP), and traditional classification algorithms, achieves a 97.88% precision. The research process amasses the NRD2024 dataset, which contains continuous detection information over 20 days for 1,500,000 newly registered domain names across 6 directions. Results from our case study demonstrate that this method also maintains a forecast precision of over 70% for PGDN that are delayed in usage after registration.
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