  In the case of malware analysis, categorization of malicious files is an
essential part after malware detection. Numerous static and dynamic techniques
have been reported so far for categorizing malware. This research presents a
deep learning-based malware detection (DLMD) technique based on static methods
for classifying different malware families. The proposed DLMD technique uses
both the byte and ASM files for feature engineering, thus classifying malware
families. First, features are extracted from byte files using two different
Deep Convolutional Neural Networks (CNN). After that, essential and
discriminative opcode features are selected using a wrapper-based mechanism,
where Support Vector Machine (SVM) is used as a classifier. The idea is to
construct a hybrid feature space by combining the different feature spaces to
overcome the shortcoming of particular feature space and thus, reduce the
chances of missing a malware. Finally, the hybrid feature space is used to
train a Multilayer Perceptron, which classifies all nine different malware
families. Experimental results show that proposed DLMD technique achieves
log-loss of 0.09 for ten independent runs. Moreover, the proposed DLMD
technique's performance is compared against different classifiers and shows its
effectiveness in categorizing malware. The relevant code and database can be
found at
https://github.com/cyberhunters/Malware-Detection-Using-Machine-Learning.
