Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware

Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ran...

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Autores principales:	Tanya Gera, Jaiteg Singh, Abolfazl Mehbodniya, Julian L. Webber, Mohammad Shabaz, Deepak Thakur
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/4496f22e45fd4287b8b0b7aebb9d72f1
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id	oai:doaj.org-article:4496f22e45fd4287b8b0b7aebb9d72f1
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spelling	oai:doaj.org-article:4496f22e45fd4287b8b0b7aebb9d72f12021-11-22T01:10:57ZDominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware1939-012210.1155/2021/7035233https://doaj.org/article/4496f22e45fd4287b8b0b7aebb9d72f12021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7035233https://doaj.org/toc/1939-0122Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance.Tanya GeraJaiteg SinghAbolfazl MehbodniyaJulian L. WebberMohammad ShabazDeepak ThakurHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Tanya Gera Jaiteg Singh Abolfazl Mehbodniya Julian L. Webber Mohammad Shabaz Deepak Thakur Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware
description	Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance.
format	article
author	Tanya Gera Jaiteg Singh Abolfazl Mehbodniya Julian L. Webber Mohammad Shabaz Deepak Thakur
author_facet	Tanya Gera Jaiteg Singh Abolfazl Mehbodniya Julian L. Webber Mohammad Shabaz Deepak Thakur
author_sort	Tanya Gera
title	Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware
title_short	Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware
title_full	Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware
title_fullStr	Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware
title_full_unstemmed	Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware
title_sort	dominant feature selection and machine learning-based hybrid approach to analyze android ransomware
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/4496f22e45fd4287b8b0b7aebb9d72f1
work_keys_str_mv	AT tanyagera dominantfeatureselectionandmachinelearningbasedhybridapproachtoanalyzeandroidransomware AT jaitegsingh dominantfeatureselectionandmachinelearningbasedhybridapproachtoanalyzeandroidransomware AT abolfazlmehbodniya dominantfeatureselectionandmachinelearningbasedhybridapproachtoanalyzeandroidransomware AT julianlwebber dominantfeatureselectionandmachinelearningbasedhybridapproachtoanalyzeandroidransomware AT mohammadshabaz dominantfeatureselectionandmachinelearningbasedhybridapproachtoanalyzeandroidransomware AT deepakthakur dominantfeatureselectionandmachinelearningbasedhybridapproachtoanalyzeandroidransomware
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Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware

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