Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the perfor...

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Autores principales:	Jaehyeong Lee, Hyuk Jang, Sungmin Ha, Yourim Yoon
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	android malware detection machine learning genetic algorithm feature selection static analysis Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/cc88efdfb8064a069f3b63b14def0b80
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spelling	oai:doaj.org-article:cc88efdfb8064a069f3b63b14def0b802021-11-11T18:20:50ZAndroid Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm10.3390/math92128132227-7390https://doaj.org/article/cc88efdfb8064a069f3b63b14def0b802021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2813https://doaj.org/toc/2227-7390Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning.Jaehyeong LeeHyuk JangSungmin HaYourim YoonMDPI AGarticleandroid malware detectionmachine learninggenetic algorithmfeature selectionstatic analysisMathematicsQA1-939ENMathematics, Vol 9, Iss 2813, p 2813 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	android malware detection machine learning genetic algorithm feature selection static analysis Mathematics QA1-939
spellingShingle	android malware detection machine learning genetic algorithm feature selection static analysis Mathematics QA1-939 Jaehyeong Lee Hyuk Jang Sungmin Ha Yourim Yoon Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm
description	Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning.
format	article
author	Jaehyeong Lee Hyuk Jang Sungmin Ha Yourim Yoon
author_facet	Jaehyeong Lee Hyuk Jang Sungmin Ha Yourim Yoon
author_sort	Jaehyeong Lee
title	Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm
title_short	Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm
title_full	Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm
title_fullStr	Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm
title_full_unstemmed	Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm
title_sort	android malware detection using machine learning with feature selection based on the genetic algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cc88efdfb8064a069f3b63b14def0b80
work_keys_str_mv	AT jaehyeonglee androidmalwaredetectionusingmachinelearningwithfeatureselectionbasedonthegeneticalgorithm AT hyukjang androidmalwaredetectionusingmachinelearningwithfeatureselectionbasedonthegeneticalgorithm AT sungminha androidmalwaredetectionusingmachinelearningwithfeatureselectionbasedonthegeneticalgorithm AT yourimyoon androidmalwaredetectionusingmachinelearningwithfeatureselectionbasedonthegeneticalgorithm
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Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

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