Malbert: A novel pre-training method for malware detection

Abstract

Microsoft’s Windows desktop operating system has been the most popular operating system in the domain of personal computers in recent years. The popularity of this system has also led to a large amount of malware on the Windows platform. With the continuous exploration of malware authors, the methods of malicious software for attacking the operating system and code obfuscation anti-detection technologies are constantly updated, making malware detection increasingly difficult. In this paper, we proposed Malbert, a pre-trained deep learning model-based method to detect malicious Windows software through dynamic analysis. Experiments were implemented on two different datasets with more than 40000 samples. We compared Malbert with some existing malware detection models, including traditional machine learning-based and deep learning-based models. The experiment also deployed a robustness test to judge whether the models can resist perturbed test samples. The results show that Malbert reaches a 99.9% detection rate on both datasets and a detection rate exceeding 98% under different robustness tests. The results also highlight the importance of pre-training in deep learning-based malware detection models as Malbert outperforms the existing state-of-the-art approaches.