标题： PhreshPhish：一个现实世界、高质量、大规模的钓鱼网站数据集和基准 显示英文标题

标题： PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark

摘要： 网络钓鱼仍然是一个普遍且不断增长的威胁，造成了严重的经济损失和声誉损害。 虽然机器学习在实时检测网络钓鱼攻击方面已经有效，但由于缺乏大规模、高质量的数据集和基准测试，进展受到阻碍。 除了由于数据收集的挑战而导致的质量问题外，现有数据集还存在泄露和不现实的基础率问题，导致性能结果过于乐观。 在本文中，我们介绍了PhreshPhish，这是一个大规模、高质量的网络钓鱼网站数据集，解决了这些限制。 与现有的公共数据集相比，PhreshPhish在规模和质量上都有显著提升，这通过无效或错误标记数据点的估计率来衡量。 此外，我们提出了一套全面的基准数据集，专门设计用于通过最小化泄露、增加任务难度、增强数据集多样性以及调整更可能在现实世界中看到的基础率来进行现实模型评估。 我们训练和评估多种解决方案方法，以在基准集上提供基线性能。 我们认为，该数据集和基准的可用性将促进现实、标准化的模型比较，并推动网络钓鱼检测的进一步发展。 数据集和基准可在Hugging Face上获得 (https://huggingface.co/datasets/phreshphish/phreshphish). 显示英文摘要

摘要： Phishing remains a pervasive and growing threat, inflicting heavy economic and reputational damage. While machine learning has been effective in real-time detection of phishing attacks, progress is hindered by lack of large, high-quality datasets and benchmarks. In addition to poor-quality due to challenges in data collection, existing datasets suffer from leakage and unrealistic base rates, leading to overly optimistic performance results. In this paper, we introduce PhreshPhish, a large-scale, high-quality dataset of phishing websites that addresses these limitations. Compared to existing public datasets, PhreshPhish is substantially larger and provides significantly higher quality, as measured by the estimated rate of invalid or mislabeled data points. Additionally, we propose a comprehensive suite of benchmark datasets specifically designed for realistic model evaluation by minimizing leakage, increasing task difficulty, enhancing dataset diversity, and adjustment of base rates more likely to be seen in the real world. We train and evaluate multiple solution approaches to provide baseline performance on the benchmark sets. We believe the availability of this dataset and benchmarks will enable realistic, standardized model comparison and foster further advances in phishing detection. The datasets and benchmarks are available on Hugging Face (https://huggingface.co/datasets/phreshphish/phreshphish).