# Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning

arXiv: https://arxiv.org/abs/2101.00521 · HTML: https://mlsanity.com/paper/2101.00521/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://mlsanity.com/paper/1703.10717/) (2017)
- [DeepDGA: Adversarially-Tuned Domain Generation and Detection](https://mlsanity.com/paper/1610.01969/) (2016)
- [Predicting Domain Generation Algorithms with Long Short-Term Memory Networks](https://mlsanity.com/paper/1611.00791/) (2016)
- [A Survey on Malicious Domains Detection through DNS Data Analysis](https://mlsanity.com/paper/1805.08426/) (2018)
- [Expansion of Cyber Attack Data From Unbalanced Datasets Using Generative Techniques](https://mlsanity.com/paper/1912.04549/) (2019)
- [Automatic Detection of Malware-Generated Domains with Recurrent Neural Models](https://mlsanity.com/paper/1709.07102/) (2017)
- [Practical Fast Gradient Sign Attack against Mammographic Image Classifier](https://mlsanity.com/paper/2001.09610/) (2020)
- [Detecting DGA domains with recurrent neural networks and side information](https://mlsanity.com/paper/1810.02023/) (2018)

## Built on by (most cited first, 3 of 3)

- [Privacy Protection of Grid Users Data with Blockchain and Adversarial Machine Learning](https://mlsanity.com/paper/2101.06308/) (2021)
- [Can Features for Phishing URL Detection Be Trusted Across Diverse Datasets? A Case Study with Explainable AI](https://mlsanity.com/paper/2411.09813/) (2024)
- [FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques](https://mlsanity.com/paper/2208.05615/) (2022)

## Abstract (arXiv)

Domain Generation Algorithms (DGAs) are used by adversaries to establish Command and Control (C&C) server communications during cyber attacks. Blacklists of known/identified C&C domains are often used as one of the defense mechanisms. However, since blacklists are static and generated by signature-based approaches, they can neither keep up nor detect never-seen-before malicious domain names. Due to this shortcoming of blacklist domain checking, machine learning algorithms have been used to address the problem to some extent. However, when training is performed with limited datasets, the algorithms are likely to fail in detecting new DGA variants. To mitigate this weakness, we successfully applied a DGA-based malicious domain classifier using the Long Short-Term Memory (LSTM) method with a novel feature engineering technique. Our model's performance shows a higher level of accuracy compared to a previously reported model from prior research. Additionally, we propose a new method using adversarial machine learning to generate never-before-seen malware-related domain families that can be used to illustrate the shortcomings of machine learning algorithms in this regard. Next, we augment the training dataset with new samples such that it makes training of the machine learning models more effective in detecting never-before-seen malicious domain name variants. Finally, to protect blacklists of malicious domain names from disclosure and tampering, we devise secure data containers that store blacklists and guarantee their protection against adversarial access and modifications.