用双向LSTM分析网址序列，精准识别钓鱼链接。
Phishing URL Detection using Bi-LSTM
- 通过双向LSTM捕捉网址字符的上下文顺序信息
- 在超65万条数据上达到97%准确率
- 适合需要低误报率的网络安全系统
网络钓鱼攻击威胁在线用户安全，常导致数据泄露、财务损失和身份盗用。传统检测系统存在误报率高、识别类型有限等问题。本文提出一种基于双向长短期记忆网络（Bi-LSTM）的深度学习方法，将网址分类为良性、钓鱼、篡改和恶意软件四类。该模型利用网址的序列特征，捕获上下文信息，显著提升检测准确性。在包含超过65万条网址的数据集上的实验表明，模型达到97%的准确率，相较于传统方法有显著提升。
原文摘要 · Abstract (English)
Phishing attacks threaten online users, often leading to data breaches, financial losses, and identity theft. Traditional phishing detection systems struggle with high false positive rates and are usually limited by the types of attacks they can identify. This paper proposes a deep learning-based approach using a Bidirectional Long Short-Term Memory (Bi-LSTM) network to classify URLs into four categories: benign, phishing, defacement, and malware. The model leverages sequential URL data and captures contextual information, improving the accuracy of phishing detection. Experimental results on a dataset comprising over 650,000 URLs demonstrate the model's effectiveness, achieving 97% accuracy and significant improvements over traditional techniques.
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