Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees

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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Bibliographic Details
Main Authors: Fahad Alswaina, Khaled Elleithy
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Malware application
reverse engineering
machine learning
static analysis
android permissions
android security
Online Access:https://ieeexplore.ieee.org/document/8552361/
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spelling 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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