  Recently, Deep Learning has been showing promising results in various
Artificial Intelligence applications like image recognition, natural language
processing, language modeling, neural machine translation, etc. Although, in
general, it is computationally more expensive as compared to classical machine
learning techniques, their results are found to be more effective in some
cases. Therefore, in this paper, we investigated and compared one of the Deep
Learning Architecture called Deep Neural Network (DNN) with the classical
Random Forest (RF) machine learning algorithm for the malware classification.
We studied the performance of the classical RF and DNN with 2, 4 & 7 layers
architectures with the four different feature sets, and found that irrespective
of the features inputs, the classical RF accuracy outperforms the DNN.
