UGC Approved Journal no 63975(19)

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

Volume 10 Issue 4
April-2023
eISSN: 2349-5162

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

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Published Paper ID:
JETIR2304C01


Registration ID:
514184

Page Number

m1-m4

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Title

MALWARE DETECTION USING MACHINE LEARNING

Abstract

With the growing popularity of Android devices, the threat of malware poses a significant risk to users' system integrity and privacy. To address this issue, a web-based framework has been developed to detect malware from Android devices. The proposed framework uses feature selection approaches to detect malware from real-world apps to select the most relevant features. These selected features and using some machine learning algorithms are made use of to build a model, including deep learning, farthest first clustering, Y-MLP, and nonlinear ensemble decision tree forest. Additionally, rough set analysis is used as a feature subset selection algorithm. The empirical data indicates that the model achieved a detection rate of 86% for identifying malware in real-world apps when all four machine-learning algorithms were used in parallel. This approach is highly effective and efficient in detecting malware belonging to unknown families. Overall, the web-based framework is an essential step in the fight against malware and provides an effective and efficient method for detecting malware from Android devices.

Key Words

Web Based Detection, API, Feature Selection Methods, Machine Learning, Y-MLP

Cite This Article

"MALWARE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.m1-m4, April-2023, Available :http://www.jetir.org/papers/JETIR2304C01.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

"MALWARE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppm1-m4, April-2023, Available at : http://www.jetir.org/papers/JETIR2304C01.pdf

Publication Details

Published Paper ID: JETIR2304C01
Registration ID: 514184
Published In: Volume 10 | Issue 4 | Year April-2023
DOI (Digital Object Identifier):
Page No: m1-m4
Country: Bangalore, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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