Malware classification method based on static multiple-feature fusion - Bo-wen SUN; Yan-yi HUANG; Qiao-kun WEN; Bin TIAN; Peng WU; Qi LI (网络与信息安全学报)
In recent years,the amount of the malwares has tended to rise explosively.New malicious samples emerge as variability and polymorphism.By means of polymorphism,shelling and confusion,traditional ways of detecting can be avoided.On the basis of massive malicious samples,a safe and efficient method was designed to classify the mal-wares.Extracting three static features including file byte features,assembly features and PE features,as well as im-proving generalization of the model through feature fusion and ensemble learning,which realized the complementarity between the features and the classifier.The experiments show that the sample achieve a stable F1-socre (93.56%).