A Lightweight Malware Detection Model Based on Knowledge Distillation

The extremely destructive nature of malware has become a major threat to Internet security. The research on malware detection techniques has been evolving. Deep learning-based malware detection methods have achieved good results by using large-scale, pre-trained models. However, these models are complex, have large parameters, and require a large amount of hardware resources and have a high inference time cost when applied. To address this challenge, this paper proposes DistillMal, a new method for lightweight malware detection based on knowledge distillation, which improves performance by using a student network to learn valuable cueing knowledge from a teacher network to achieve a lightweight model. We conducted extensive experiments on two new datasets and showed that the student network model's performance is very close to that of the original model and the outperforms it on some metrics. Our approach helps address the resource constraints and computational challenges faced by traditional deep learning large models. Our research highlights the potential of using knowledge distillation to develop lightweight malware detection models.