用微调技术让小模型精准识别钓鱼邮件，效果远超同类工具。
Phishsense-1B: A Technical Perspective on an AI-Powered Phishing Detection Model
- 基于LoRA和推理优化方法微调小模型，提升安全检测能力。
- 在自建数据集上准确率达97.5%，真实场景下仍保持70%准确率。
- 适合安全团队快速部署，也适用于资源受限环境的模型优化。
钓鱼攻击是当前数字环境中的持续性网络安全威胁。本文介绍Phishsense-1B，它是对Llama-Guard-3-1B模型的优化版本，专为钓鱼邮件检测与推理设计。该模型采用低秩适应（LoRA）和GuardReasoner微调方法，构建了平衡的钓鱼与正常邮件数据集，并展示了显著性能提升。实验结果表明，Phishsense-1B在自建数据集上达到97.5%的准确率，在具有挑战性的现实数据集上仍保持70%的准确率，明显优于原始模型及BERT基线检测器。同时，本文还对比了提示工程与微调策略的优劣，并探讨了潜在部署场景。
原文摘要 · Abstract (English)
Phishing is a persistent cybersecurity threat in today's digital landscape. This paper introduces Phishsense-1B, a refined version of the Llama-Guard-3-1B model, specifically tailored for phishing detection and reasoning. This adaptation utilizes Low-Rank Adaptation (LoRA) and the GuardReasoner finetuning methodology. We outline our LoRA-based fine-tuning process, describe the balanced dataset comprising phishing and benign emails, and highlight significant performance improvements over the original model. Our findings indicate that Phishsense-1B achieves an impressive 97.5% accuracy on a custom dataset and maintains strong performance with 70% accuracy on a challenging real-world dataset. This performance notably surpasses both unadapted models and BERT-based detectors. Additionally, we examine current state-of-the-art detection methods, compare prompt-engineering with fine-tuning strategies, and explore potential deployment scenarios.
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