用字符级CNN和特征工程融合模型，实现高精度实时钓鱼网址检测。
Phishing Detection System: An Ensemble Approach Using Character-Level CNN and Feature Engineering
- 结合字符级CNN与人工特征，通过集成学习提取深层网页特征。
- 在1.98万条数据上达到99.8%准确率，精确率高达100%。
- 适合需要低误报率的网络安全系统部署，尤其对抗新型钓鱼手段。
当前网络钓鱼攻击仍是主要安全威胁，攻击者不断变换策略以欺骗用户。本文提出一种基于集成学习的钓鱼检测系统，融合字符级卷积神经网络（CNN）与轻量梯度提升机（LightGBM），并利用从URL中提取的36个词法、结构及域名特征。在包含19,873条URL的测试集上，该模型实现99.819%准确率、100%精确率、99.635%召回率以及99.947%的ROC-AUC值。系统通过基于FastAPI的实时服务与直观界面部署，支持在线检测。实验表明，集成模型性能优于单一模型，其中LightGBM贡献40%，字符级CNN贡献60%。该方法在保持极低误报率的同时，有效识别现代钓鱼技术。
原文摘要 · Abstract (English)
In actuality, phishing attacks remain one of the most prevalent cybersecurity risks in existence today, with malevolent actors constantly changing their strategies to successfully trick users. This paper presents an AI model for a phishing detection system that uses an ensemble approach to combine character-level Convolutional Neural Networks (CNN) and LightGBM with engineered features. Our system uses a character-level CNN to extract sequential features after extracting 36 lexical, structural, and domain-based features from the URLs. On a test dataset of 19,873 URLs, the ensemble model achieves an accuracy of 99.819 percent, precision of 100 percent, recall of 99.635 percent, and ROC-AUC of 99.947 percent. Through a FastAPI-based service with an intuitive user interface, the suggested system has been utilised to offer real-time detection. In contrast, the results demonstrate that the suggested solution performs better than individual models; LightGBM contributes 40 percent and character-CNN contributes 60 percent to the final prediction. The suggested method maintains extremely low false positive rates while doing a good job of identifying contemporary phishing techniques. Index Terms - Phishing detection, machine learning, deep learning, CNN, ensemble methods, cybersecurity, URL analysis
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