Abstract: With the rapid increment of the number of Android malwares,the traditional malware detection and classification methods were proved to be with low detection rate,highly complex training model and so on.To solve above problems,the texture feature of gray-scale image-based malware classification method was proposed by combining the image texture feature abstraction and machine learning classifiers.The proposed method starts with converting the malware samples into grayscale images.Four feature abstraction methods were designed including GIST and Tamura-based feature abstraction algorithm.By taking the texture feature as the source data,5 kinds of classification learning models were constructed by using high performance architecture Caffe.Finally,the detection and classification of malwares were done.The experimental results show that the image texture feature-based malware classification achieves high accuracy,and the Caffe architecture can effectively improve the learning time which further reduces the complexity.

Key words: Classification learning, Gray-scale images, Malwares, Texture feature