A New Multitasking Malware Classification Model Based on Feature Fusion

In this paper, In this paper, we achieved a new Multitask Malware Classification Model Based on DNN (Deep Neural Network), we use the API functions extracted from the dynamic analysis as the main features, the static extracted n-gram, entropy, SYM, Metadata, asm-image and grayscale image as the fusion features to construct a classifier that can deal with the dichotomy problem and the family classification problem. The system was trained in 12 malware families, 3,792 malicious samples and 138 benign samples. The accuracy of the dichotomous problem is 99.61%. The proposed malware family classification model also achieves 99.33% malware family classification error rate.