Research on a High Robust Detection Model for Malicious Software
In recent years,malware has become increasingly harmful to the security of cyberspace.In order to cope with large-scale malware detection tasks in the network environment,researchers have proposed automatic detection methods based on machine learning and deep learning.However,these methods need to spend more time on feature engineering,resulting in low detection efficiency.At the same time,the existence of malware countersamples also affects these methods to make correct judgments,causing harm to information security.Therefore,this paper proposed a robust malware detection method(MDCAM).This method firstly analyzed the characteristics of different families of malware and malware adversarial examples based on code visualization technology,and then builded a detection model that integrated improved ConvNeXt network,mixed domain attention mechanism and FocalLoss function,which significantly improved the comprehensive ability and robustness of the detection model.
NETL
NSTL
万方数据
