Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection

TL;DR

This paper compares deep neural networks and classical random forest algorithms for malware detection, finding that random forests outperform DNNs across various feature sets and architectures.

Contribution

It provides a comparative analysis of DNN and RF for malware classification, highlighting the superior performance of RF in this context.

Findings

Random Forest outperforms DNN in malware detection accuracy.

Increasing DNN layers does not improve performance over RF.

Feature set variations do not significantly change RF's superiority.

Abstract

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.