This paper proposes a malware detection method based on incremental learning,which can not only reduce the model size and use of system resources,but also ensure the accuracy.Moreover,on the basis of effectively reducing the training time of the model,it can effectively solve the catastrophic forgetting problem and concept drift phenomenon caused by unbalanced data flow that most deep learning algorithms face.The binary files of benign and malicious codes are first converted into RGB three-channel color maps,and then image features are extracted for incremental training.The training process is divided into two stages,which are training convolution layer and full connection layer,and using linear model to correct the residual in the deviation correction layer.Experimental results show that the accuracy rate of malware detection is 95.8%,which can effectively improve the classification accuracy,so it can be well used in malware detection.
维普
万方数据