# False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA Classifiers

arXiv: https://arxiv.org/abs/2307.04358 · HTML: https://mlsanity.com/paper/2307.04358/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 23 of 23)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [An Investigation of Why Overparameterization Exacerbates Spurious Correlations](https://mlsanity.com/paper/2005.04345/) (2020)
- [Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks](https://mlsanity.com/paper/1907.04595/) (2019)
- [A Diagnostic Study of Explainability Techniques for Text Classification](https://mlsanity.com/paper/2009.13295/) (2020)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [B-cos Networks: Alignment is All We Need for Interpretability](https://mlsanity.com/paper/2205.10268/) (2022)
- [Excessive Invariance Causes Adversarial Vulnerability](https://mlsanity.com/paper/1811.00401/) (2018)
- [Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation](https://mlsanity.com/paper/1806.01156/) (2018)
- [iNNvestigate neural networks!](https://mlsanity.com/paper/1808.04260/) (2018)
- [Evaluating Explanation Methods for Deep Learning in Security](https://mlsanity.com/paper/1906.02108/) (2019)
- [eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys](https://mlsanity.com/paper/1702.08568/) (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)
- [Analyzing the Real-World Applicability of DGA Classifiers](https://mlsanity.com/paper/2006.11103/) (2020)
- [CharBot: A Simple and Effective Method for Evading DGA Classifiers](https://mlsanity.com/paper/1905.01078/) (2019)
- [Making Use of NXt to Nothing: The Effect of Class Imbalances on DGA Detection Classifiers](https://mlsanity.com/paper/2007.00300/) (2020)
- [First Step Towards EXPLAINable DGA Multiclass Classification](https://mlsanity.com/paper/2106.12336/) (2021)
- [Explaining Machine Learning DGA Detectors from DNS Traffic Data](https://mlsanity.com/paper/2208.05285/) (2022)

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

- [Towards Robust Domain Generation Algorithm Classification](https://mlsanity.com/paper/2404.06236/) (2024)
- [Feature Attribution in 5G Intrusion Detection: A Statistical vs. Logic-Based Comparison](https://mlsanity.com/paper/2509.10206/) (2025)
- [DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection](https://mlsanity.com/paper/2605.10436/) (2026)

## Abstract (arXiv)

The problem of revealing botnet activity through Domain Generation Algorithm (DGA) detection seems to be solved, considering that available deep learning classifiers achieve accuracies of over 99.9%. However, these classifiers provide a false sense of security as they are heavily biased and allow for trivial detection bypass. In this work, we leverage explainable artificial intelligence (XAI) methods to analyze the reasoning of deep learning classifiers and to systematically reveal such biases. We show that eliminating these biases from DGA classifiers considerably deteriorates their performance. Nevertheless we are able to design a context-aware detection system that is free of the identified biases and maintains the detection rate of state-of-the art deep learning classifiers. In this context, we propose a visual analysis system that helps to better understand a classifier's reasoning, thereby increasing trust in and transparency of detection methods and facilitating decision-making.