An efficient classification of malware behavior using deep neural network

Malware detection have long become a challenge in research. The existing methods rely on malware signature which are proved not to be effective nowadays. The recent researches focus on using probabilistic model such as machine learning to detect the existence of malware. They, however, do not achiev...

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Published in:Journal of intelligent & fuzzy systems 2018-01, Vol.35 (6), p.5801-5814
Main Authors: Hai, Quan Tran, Hwang, Seong Oun
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Experimentation
Machine learning
Malware
Neural networks
Probabilistic models
State of the art
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description Malware detection have long become a challenge in research. The existing methods rely on malware signature which are proved not to be effective nowadays. The recent researches focus on using probabilistic model such as machine learning to detect the existence of malware. They, however, do not achieve such a good performance. Particularly, machine learning techniques still have an issue of high feature engineering overhead. In this paper, we propose a deep learning method to detect malware based on their malicious behavior. Through experimentation, we show that our method can achieve a very high accuracy rate of 98.75 in F1 measure, compared to state of the art methods.
doi_str_mv 10.3233/JIFS-169823
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subjects Artificial intelligence
Artificial neural networks
Experimentation
Machine learning
Malware
Neural networks
Probabilistic models
State of the art
title An efficient classification of malware behavior using deep neural network
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