Method of Malware Family Classification Based on Attention-DenseNet-BC Model Mechanism

Malware is one of the most serious threats to the Internet.The existing malware has huge data size and various features.Convolutional Neural Network has the features of autonomous learning,which can be used to solve the problems that the feature extraction of malware is complex and the feature selection is difficult.However,in convolutional neural network,conti-nuously increasing the network layers will cause a disappear of the gradient,leading to a degradation of network performance and low accuracy.To solve this problem,an Attention-DenseNet-BC model that is suitable for malware image detection is proposed.First,the Attention-DenseNet-BC model is constructed by combining the DenseNet-BC network and the attention mechanism.Then,the malware images are used as the input of the model,and the detection results are obtained by training and testing the model.The experimental results indicate that compared with other deep learning models,the Attention-DenseNet-BC model can achieve better classification results.A high classification accuracy can be attained with the model based on the malimg public dataset