Sharing FANCI Features: A Privacy Analysis of Feature Extraction for DGA Detection

TL;DR

This paper evaluates the privacy risks of sharing features from FANCI, a DGA detection tool, finding that the feature extraction process offers strong privacy protection against domain name reconstruction attacks.

Contribution

The study provides an empirical and theoretical analysis demonstrating that FANCI's feature extractor resists inversion attacks, supporting privacy-preserving sharing of features.

Findings

Reconstruction attacks on FANCI features perform poorly.

Mathematical analysis supports the robustness of FANCI's feature extraction.

Sharing FANCI features does not significantly compromise privacy.

Abstract

The goal of Domain Generation Algorithm (DGA) detection is to recognize infections with bot malware and is often done with help of Machine Learning approaches that classify non-resolving Domain Name System (DNS) traffic and are trained on possibly sensitive data. In parallel, the rise of privacy research in the Machine Learning world leads to privacy-preserving measures that are tightly coupled with a deep learning model's architecture or training routine, while non deep learning approaches are commonly better suited for the application of privacy-enhancing methods outside the actual classification module. In this work, we aim to measure the privacy capability of the feature extractor of feature-based DGA detector FANCI (Feature-based Automated Nxdomain Classification and Intelligence). Our goal is to assess whether a data-rich adversary can learn an inverse mapping of FANCI's feature extractor and thereby reconstruct domain names from feature vectors. Attack success would pose a privacy threat to sharing FANCI's feature representation, while the opposite would enable this representation to be shared without privacy concerns. Using three real-world data sets, we train a recurrent Machine Learning model on the reconstruction task. Our approaches result in poor reconstruction performance and we attempt to back our findings with a mathematical review of the feature extraction process. We thus reckon that sharing FANCI's feature representation does not constitute a considerable privacy leakage.