DGA-based botnet detection toward imbalanced multiclass learning

Abstract

Botnets based on the Domain Generation Algorithm (DGA) mechanism pose great challenges to the main current detection methods because of their strong concealment and robustness. However, the complexity of the DGA family and the imbalance of samples continue to impede research on DGA detection. In the existing work, the sample size of each DGA family is regarded as the most important determinant of the resampling proportion; thus, differences in the characteristics of various samples are ignored, and the optimal resampling effect is not achieved. In this paper, a Long Short-Term Memory-based Property and Quantity Dependent Optimization (LSTM.PQDO) method is proposed. This method takes advantage of LSTM to automatically mine the comprehensive features of DGA domain names. It iterates the resampling proportion with the optimal solution based on a comprehensive consideration of the original number and characteristics of the samples to heuristically search for a better solution around the initial solution in the right direction; thus, dynamic optimization of the resampling proportion is realized. The experimental results show that the LSTM.PQDO method can achieve better performance compared with existing models to overcome the difficulties of unbalanced datasets; moreover, it can function as a reference for sample resampling tasks in similar scenarios.