First Step Towards EXPLAINable DGA Multiclass Classification

TL;DR

This paper introduces EXPLAIN, a feature-based DGA multiclass classifier that offers competitive accuracy, real-time performance, and improved interpretability over deep learning models for malware domain detection.

Contribution

The paper presents a novel feature-based classifier for DGA detection that enhances interpretability and maintains competitive performance compared to deep learning approaches.

Findings

Achieves competitive classification accuracy

Operates in real-time for practical deployment

Offers improved interpretability of predictions

Abstract

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.