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In the case of malware analysis, categorization of malicious files is anessential part after malware detection. Numerous static and dynamic techniqueshave been reported so far for categorizing malware. This research presents adeep learning-based malware detection (DLMD) technique based on static methodsfor classifying different malware families. The proposed DLMD technique usesboth the byte and ASM files for feature engineering, thus classifying malwarefamilies. First, features are extracted from byte files using two differentDeep Convolutional Neural Networks (CNN). After that, essential anddiscriminative opcode features are selected using a wrapper-based mechanism,where Support Vector Machine (SVM) is used as a classifier. The idea is toconstruct a hybrid feature space by combining the different feature spaces toovercome the shortcoming of particular feature space and thus, reduce thechances of missing a malware. Finally, the hybrid feature space is used totrain a Multilayer Perceptron, which classifies all nine different malwarefamilies. Experimental results show that proposed DLMD technique achieveslog-loss of 0.09 for ten independent runs. Moreover, the proposed DLMDtechnique's performance is compared against different classifiers and shows itseffectiveness in categorizing malware. The relevant code and database can befound athttps://github.com/cyberhunters/Malware-Detection-Using-Machine-Learning.

Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique