Comparison of Deep Learning and the Classical Machine Learning Algorithm
for the Malware Detection
Hemant Rathore Sanjay K. Sahay Mohit Sewak
作者信息
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.引用本文复制引用
Hemant Rathore,Sanjay K. Sahay,Mohit Sewak.Comparison of Deep Learning and the Classical Machine Learning Algorithm
for the Malware Detection[EB/OL].(2018-09-16)[2026-04-04].https://arxiv.org/abs/1809.05889.学科分类
计算技术、计算机技术
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