BERT-Enhanced DGA Botnet Detection: A Comparative Analysis of Machine Learning and Deep Learning Models

Abstract

With the proliferation of Internet of Things, detecting Domain Generation Algorithm (DGA) botnets is critical for protecting networks from evolving and sophisticated cybersecurity threats. This paper explores a novel approach combining BERT with machine learning and deep learning techniques to detect DGA botnets. We provide a comprehensive benchmark by evaluating the performance of various BERT versions and detection methods on diverse datasets. Our experimental results reveal the significant impact of BERT version selection on detection accuracy and the superior performance of deep learning models, such as CNN, MLP, and LSTM, compared to conventional machine learning models. These findings highlight the potential of BERT and deep learning in improving DGA botnet detection and offer valuable insights for future research in this area.