Cryptocurrency Mining Malware Detection Method Based on Sample Embedding
Due to its high profitability and anonymity,cryptocurrency mining malware poses a great threat and loss to computer users.In order to confront the threat posed by mining malware,machine learning detectors based on software static features usually select a single type of static features,or integrate the detection results of different kinds of static features through inte-grated learning,ignoring the internal relationship between different kinds of static features,and its detection rate remains to be discussed.This paper starts from the internal hierarchical relationship of mining malware.It extracts basic blocks,control flow graphs and function call graphs of samples as static features,trains the three-layer model to embed these features into the vector respectively,and gradually gathers the features from the bottom to the top,and finally sends top features to the classifier to detect mining malware.To simulate the detection situation in real world,it first trains the model on a relatively smaller experimental da-ta set,and then tests the performance of the model on another much larger data set.Experiment results show that the perfor-mance of th proposed method is much better than that of some machine learning models proposed in recent years.The recall rate and accuracy rate of three-layer-embedding model is more than 7％and 3％higher than that of other models,respectively.
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