双路径钓鱼检测：融合基于Transformer的NLP与结构化URL分析
Dual-Path Phishing Detection: Integrating Transformer-Based NLP with Structural URL Analysis
AI总结：
本文提出一种双路径钓鱼检测框架，融合DistilBERT语义分析与随机森林URL结构分析，显著提升检测准确率，兼具模块化与可解释性。
摘要：
钓鱼邮件构成持续且日益复杂的威胁，通过利用语义和结构漏洞的欺骗手段破坏电子邮件安全。传统的检测方法通常基于对邮件内容或嵌入URL的孤立分析，无法全面应对这些不断演变的攻击。在本文中，我们提出了一种双路径钓鱼检测框架，该框架将基于Transformer的自然语言处理（NLP）与经典机器学习相结合，以联合分析邮件文本和嵌入的URL。我们的方法利用了微调Transformer架构（如DistilBERT）进行语义分析和通过字符级TF-IDF向量化结合经典分类器（如随机森林）进行结构链接分析的互补优势。对代表性邮件和URL数据集的实证评估表明，这种组合方法显著提高了检测准确性。具体而言，DistilBERT模型在文本钓鱼检测中实现了准确性与计算效率之间的近乎最优平衡，而随机森林在识别恶意URL方面明显优于其他经典分类器。模块化设计允许独立部署或集成集成的灵活性，促进了实际应用。总的来说，我们的结果突显了这种双路径方法的有效性和实用价值，建立了一个可扩展、准确且可解释的解决方案，能够增强电子邮件安全以应对当代钓鱼威胁。
英文摘要：
Phishing emails pose a persistent and increasingly sophisticated threat, undermining email security through deceptive tactics designed to exploit both semantic and structural vulnerabilities. Traditional detection methods, often based on isolated analysis of email content or embedded URLs, fail to comprehensively address these evolving attacks. In this paper, we propose a dual-path phishing detection framework that integrates transformer-based natural language processing (NLP) with classical machine learning to jointly analyze email text and embedded URLs. Our approach leverages the complementary strengths of semantic analysis using fine-tuned transformer architectures (e.g., DistilBERT) and structural link analysis via character-level TF-IDF vectorization paired with classical classifiers (e.g., Random Forest). Empirical evaluation on representative email and URL datasets demonstrates that this combined approach significantly improves detection accuracy. Specifically, the DistilBERT model achieves a near-optimal balance between accuracy and computational efficiency for textual phishing detection, while Random Forest notably outperforms other classical classifiers in identifying malicious URLs. The modular design allows flexibility for standalone deployment or ensemble integration, facilitating real-world adoption. Collectively, our results highlight the efficacy and practical value of this dual-path approach, establishing a scalable, accurate, and interpretable solution capable of enhancing email security against contemporary phishing threats.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
