PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark
This addresses the need for better datasets and benchmarks to enable realistic model comparison and advance phishing detection, though it is incremental as it builds on existing data collection efforts.
The authors tackled the problem of limited progress in phishing detection due to poor-quality datasets by introducing PhreshPhish, a large-scale, high-quality dataset that is substantially larger and has a lower estimated rate of invalid or mislabeled data points compared to existing datasets, along with benchmark sets designed for realistic evaluation.
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).