A Image Texture And Bp Neural Network Based Malicious Files Detection Technique For Cloud Storage Systems

In a complicated cloud storage environment in which users upload a large number of files everyday, in order to better solve the challenge of inefficient malicious detection and weak adaptability of multi-platform detection in the traditional way, we propose a malicious file detection method which is based on image texture analysis and BP neural network algorithm. By combining the technology of image analysis and the malicious file detection, the malicious file is converted into grayscale image, the GLCM (Ground Launched Cruise Missile) and the GIST (Generalized Search Trees) algorithms are used to extract the texture features, and the BP neural network algorithm is then used for learning and training. In this paper, we propose and implement a malicious file detection system by means of image texture extraction. Through the experimental analysis on a large number of virus samples from the well-known VirusShare project, the experimental results show that our proposed approach has the characteristics of fast speed, high adaptability and high accuracy.