UGC Approved Journal no 63975(19)

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Published in:

Volume 8 Issue 10
October-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2110485


Registration ID:
316205

Page Number

e556-e563

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Title

A Study on Android Malware Detection Using Machine Learning

Abstract

Smart phones are becoming essential in our lives, and Android is one of the most popular operating systems. Android OS is wide-ranging in the mobile industry today because of its open source architecture. It is a wide variety of applications and basic features. App users tend to trust Android OS to secure data, but it has been shown that Android is more vulnerable and unstable. Identification of Android OS malware has become an emerging research subject of concern. This paper aims to analyse the various characteristics involved in malware detection. Mobile devices have grown exponentially in terms of functionality in the recent years, since they provide almost all the functions that a computer provides. Among the various operating systems employed, Android has become a prominent one in the recent years due to its huge user base, since the availability of android applications is free in the android application market. Due to his huge user base it has become a very likely target for attackers. Among the available applications a vast majority of them are not authenticated formally and hence are malicious. These applications may steal the private information from the user’s device. The proposed framework ensures that these kind of applications are detected at high accuracy, it provides a machine learning-based malware detection system on Android to detect the malicious applications to enhance security and privacy of smartphone users. The proposed framework monitors various permissions related to the android applications and analyses the features by using machine learning classifiers to authenticate the applications. It also addresses malware detection methods. The current detection mechanism utilizes algorithms such as SVM, Neural Networks and other algorithms for machine learning to train the sets and find the malware. The results of our empirical evaluation show our system is competitive in terms of classification accuracy and detection efficiency. At dataset Drebin (benign 5.9K and malware 5.6K) and AMD (benign 20.5K and malware 20.8K), our system has achieved 96% and 98% detection results both in accuracy and F-measure. Compared with the state-of-the-art system in detecting evolving malware called MaMaDroid on the dataset of 6.0K benign and 20.5K malicious samples spanning from 2010 to 2017, our system achieves higher accuracy while improving detection efficiency by 15 times

Key Words

APks Dataset, SVM algorithms, MLP, Malware detection

Cite This Article

"A Study on Android Malware Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.e556-e563, October-2021, Available :http://www.jetir.org/papers/JETIR2110485.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"A Study on Android Malware Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. ppe556-e563, October-2021, Available at : http://www.jetir.org/papers/JETIR2110485.pdf

Publication Details

Published Paper ID: JETIR2110485
Registration ID: 316205
Published In: Volume 8 | Issue 10 | Year October-2021
DOI (Digital Object Identifier):
Page No: e556-e563
Country: VISAKHAPATNAM, AP, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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