# Toward More Generalized Malicious URL Detection Models

arXiv: https://arxiv.org/abs/2202.10027 · HTML: https://mlsanity.com/paper/2202.10027/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 13 of 13)

- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://mlsanity.com/paper/1812.10352/) (2018)
- [The Variational Fair Autoencoder](https://mlsanity.com/paper/1511.00830/) (2015)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [Controllable Invariance through Adversarial Feature Learning](https://mlsanity.com/paper/1705.11122/) (2017)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings](https://mlsanity.com/paper/1809.02169/) (2018)
- [Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization](https://mlsanity.com/paper/1904.12543/) (2019)
- [URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection](https://mlsanity.com/paper/1802.03162/) (2018)
- [SoK: Applying Machine Learning in Security - A Survey](https://mlsanity.com/paper/1611.03186/) (2016)

## Built on by (most cited first, 9 of 9)

- [Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning](https://mlsanity.com/paper/2407.05040/) (2024)
- [Enhance Modality Robustness in Text-Centric Multimodal Alignment with Adversarial Prompting](https://mlsanity.com/paper/2408.09798/) (2024)
- [Enhance the Robustness of Text-Centric Multimodal Alignments](https://mlsanity.com/paper/2407.05036/) (2024)
- [Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection](https://mlsanity.com/paper/2505.07309/) (2025)
- [Continuous Multi-Task Pre-training for Malicious URL Detection and Webpage Classification](https://mlsanity.com/paper/2402.11495/) (2024)
- [Benchmarking Large Language Model Uncertainty for Prompt Optimization](https://mlsanity.com/paper/2409.10044/) (2024)
- [From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories](https://mlsanity.com/paper/2504.16449/) (2025)
- [WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT](https://mlsanity.com/paper/2506.19356/) (2025)
- [Multimodal Chip Physical Design Engineer Assistant](https://mlsanity.com/paper/2510.15872/) (2025)

## Abstract (arXiv)

This paper reveals a data bias issue that can severely affect the performance while conducting a machine learning model for malicious URL detection. We describe how such bias can be identified using interpretable machine learning techniques, and further argue that such biases naturally exist in the real world security data for training a classification model. We then propose a debiased training strategy that can be applied to most deep-learning based models to alleviate the negative effects from the biased features. The solution is based on the technique of self-supervised adversarial training to train deep neural networks learning invariant embedding from biased data. We conduct a wide range of experiments to demonstrate that the proposed strategy can lead to significantly better generalization capability for both CNN-based and RNN-based detection models.