First Step Towards EXPLAINable DGA Multiclass Classification
This work addresses the need for explainable DGA classification in cybersecurity, offering a practical solution for targeted remediation, though it is incremental as it builds on existing methods with improved interpretability.
The paper tackles the problem of classifying domain generation algorithms (DGAs) for malware detection by proposing EXPLAIN, a feature-based classifier that achieves competitive results and real-time capability while providing more interpretable predictions than existing deep learning models.
Numerous malware families rely on domain generation algorithms (DGAs) to establish a connection to their command and control (C2) server. Counteracting DGAs, several machine learning classifiers have been proposed enabling the identification of the DGA that generated a specific domain name and thus triggering targeted remediation measures. However, the proposed state-of-the-art classifiers are based on deep learning models. The black box nature of these makes it difficult to evaluate their reasoning. The resulting lack of confidence makes the utilization of such models impracticable. In this paper, we propose EXPLAIN, a feature-based and contextless DGA multiclass classifier. We comparatively evaluate several combinations of feature sets and hyperparameters for our approach against several state-of-the-art classifiers in a unified setting on the same real-world data. Our classifier achieves competitive results, is real-time capable, and its predictions are easier to trace back to features than the predictions made by the DGA multiclass classifiers proposed in related work.