Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees
Due to recent developments in hardware and software technologies for mobile phones, people depend on their smartphones more than ever before. Today, people conduct a variety of business, health, and financial transactions on their mobile devices. This trend has caused an influx of mobile application...
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doaj-418e5053f4fb4dbcac2f19110d94f05e2021-03-29T21:35:53ZengIEEEIEEE Access2169-35362018-01-016762177622710.1109/ACCESS.2018.28839758552361Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized TreesFahad Alswaina0https://orcid.org/0000-0002-8042-1598Khaled Elleithy1Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT, USAComputer Science and Engineering Department, University of Bridgeport, Bridgeport, CT, USADue to recent developments in hardware and software technologies for mobile phones, people depend on their smartphones more than ever before. Today, people conduct a variety of business, health, and financial transactions on their mobile devices. This trend has caused an influx of mobile applications that require users' sensitive information. As these applications increase so too have the number of malicious applications that steal users' sensitive information. Through our research, we developed a reverse engineering framework (RevEng). Within RevEng, the applications' permissions were selected, and then fed into machine learning algorithms. Through our research, we created a reduced set of permissions by using extremely randomized trees that achieved high accuracy and a shorter execution time. Furthermore, we conducted two approaches based on the extracted information. Approach one used binary value representation of the permissions. Approach two used the features' importance; we represented each selected permission (in approach one) by its weighted value instead of the binary value. We conducted a comparison between the results of our two approaches and other related work. Our approaches achieved better results in both accuracy and time performance with a reduced number of permissions.https://ieeexplore.ieee.org/document/8552361/Malware applicationreverse engineeringmachine learningstatic analysisandroid permissionsandroid security |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fahad Alswaina Khaled Elleithy |
spellingShingle |
Fahad Alswaina Khaled Elleithy Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees IEEE Access Malware application reverse engineering machine learning static analysis android permissions android security |
author_facet |
Fahad Alswaina Khaled Elleithy |
author_sort |
Fahad Alswaina |
title |
Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees |
title_short |
Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees |
title_full |
Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees |
title_fullStr |
Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees |
title_full_unstemmed |
Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees |
title_sort |
android malware permission-based multi-class classification using extremely randomized trees |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
Due to recent developments in hardware and software technologies for mobile phones, people depend on their smartphones more than ever before. Today, people conduct a variety of business, health, and financial transactions on their mobile devices. This trend has caused an influx of mobile applications that require users' sensitive information. As these applications increase so too have the number of malicious applications that steal users' sensitive information. Through our research, we developed a reverse engineering framework (RevEng). Within RevEng, the applications' permissions were selected, and then fed into machine learning algorithms. Through our research, we created a reduced set of permissions by using extremely randomized trees that achieved high accuracy and a shorter execution time. Furthermore, we conducted two approaches based on the extracted information. Approach one used binary value representation of the permissions. Approach two used the features' importance; we represented each selected permission (in approach one) by its weighted value instead of the binary value. We conducted a comparison between the results of our two approaches and other related work. Our approaches achieved better results in both accuracy and time performance with a reduced number of permissions. |
topic |
Malware application reverse engineering machine learning static analysis android permissions android security |
url |
https://ieeexplore.ieee.org/document/8552361/ |
work_keys_str_mv |
AT fahadalswaina androidmalwarepermissionbasedmulticlassclassificationusingextremelyrandomizedtrees AT khaledelleithy androidmalwarepermissionbasedmulticlassclassificationusingextremelyrandomizedtrees |
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1724192626286002176 |