# Towards Benchmark Datasets for Machine Learning Based Website Phishing Detection: An experimental study

arXiv: https://arxiv.org/abs/2010.12847 · HTML: https://mlsanity.com/paper/2010.12847/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data](https://mlsanity.com/paper/2402.04823/) (2024)
- [A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space](https://mlsanity.com/paper/2112.01156/) (2021)
- [Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints](https://mlsanity.com/paper/2502.18237/) (2025)
- [Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data](https://mlsanity.com/paper/2406.00775/) (2024)
- [TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases](https://mlsanity.com/paper/2408.07579/) (2024)
- [Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning](https://mlsanity.com/paper/2210.13660/) (2022)
- [Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors](https://mlsanity.com/paper/2310.03166/) (2023)
- [Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation](https://mlsanity.com/paper/2405.03537/) (2024)
- [Detecting Quishing Attacks with Machine Learning Techniques Through QR Code Analysis](https://mlsanity.com/paper/2505.03451/) (2025)
- [On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses](https://mlsanity.com/paper/2506.02978/) (2025)
- [Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data](https://mlsanity.com/paper/2311.04503/) (2023)
- [Can Features for Phishing URL Detection Be Trusted Across Diverse Datasets? A Case Study with Explainable AI](https://mlsanity.com/paper/2411.09813/) (2024)
- [Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data](https://mlsanity.com/paper/2507.10998/) (2025)
- [Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review](https://mlsanity.com/paper/2506.15506/) (2025)
- [A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data](https://mlsanity.com/paper/2512.07030/) (2025)
- [CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification](https://mlsanity.com/paper/2604.25512/) (2026)

## Abstract (arXiv)

In this paper, we present a general scheme for building reproducible and extensible datasets for website phishing detection. The aim is to (1) enable comparison of systems using different features, (2) overtake the short-lived nature of phishing websites, and (3) keep track of the evolution of phishing tactics. For experimenting the proposed scheme, we start by adopting a refined classification of website phishing features and we systematically select a total of 87 commonly recognized ones, we classify them, and we made them subjects for relevance and runtime analysis. We use the collected set of features to build a dataset in light of the proposed scheme. Thereafter, we use a conceptual replication approach to check the genericity of former findings for the built dataset. Specifically, we evaluate the performance of classifiers on individual classes and on combinations of classes, we investigate different combinations of models, and we explore the effects of filter and wrapper methods on the selection of discriminative features. The results show that Random Forest is the most predictive classifier. Features gathered from external services are found the most discriminative where features extracted from web page contents are found less distinguishing. Besides external service based features, some web page content features are found time consuming and not suitable for runtime detection. The use of hybrid features provided the best accuracy score of 96.61%. By investigating different feature selection methods, filter-based ranking together with incremental removal of less important features improved the performance up to 96.83% better than wrapper methods.