UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 7
July-2021
eISSN: 2349-5162

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

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Unique Identifier

Published Paper ID:
JETIR2107634


Registration ID:
311711

Page Number

f30-f37

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Title

Detection of Malware using Machine Learning in Android Devices/Applications

Abstract

Spreading malware through Android devices and applications became an important strategy of cyber attackers. Therefore, malware detection in Android applications has become an important area of research. In this context, it is important to answer the question that reads “how can we develop a model based on Machine Learning (ML) to detect malware in Android devices/applications?” When malware is detected in real time from Android mobile applications, it can relive the users of Android phones from the risk of malware. It will also help stakeholders of Android devices to be safe from malicious software. The proposed system extracts feature from. APK files and training is given for supervised learning. Different ML models like Multinomial Naïve Bayes, Random Forest and SVM are used as prediction models. With these ML techniques a framework is realized to have provision for protection of malware in Android devices or applications. The proposed solution continues giving support with increased quality. The rationale behind this is that as the applications are protected and malware is detected, the training data gets increased. With increased training data, it will become much more accurate as time goes on. With some changes, it can be made to detect Android applications live when it is associated with a competing device.

Key Words

Malware Detection , feature extraction, machine learning, SVM, Random Forest, Multinomial Naïve Bayes

Cite This Article

"Detection of Malware using Machine Learning in Android Devices/Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.f30-f37, July-2021, Available :http://www.jetir.org/papers/JETIR2107634.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

"Detection of Malware using Machine Learning in Android Devices/Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppf30-f37, July-2021, Available at : http://www.jetir.org/papers/JETIR2107634.pdf

Publication Details

Published Paper ID: JETIR2107634
Registration ID: 311711
Published In: Volume 8 | Issue 7 | Year July-2021
DOI (Digital Object Identifier):
Page No: f30-f37
Country: Medchal, Telangana, India .
Area: Engineering
ISSN Number: 2349-5162


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