UGC Approved Journal no 63975(19)

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

Volume 11 Issue 1
January-2024
eISSN: 2349-5162

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

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Published Paper ID:
JETIR2401437


Registration ID:
531699

Page Number

e306-e325

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Title

ANDROID MALWARE ANALYSIS – A LITERATURE REVIEW

Abstract

Background/Purpose: The ever-increasing presence of malicious software designed to target Android devices represents a huge risk to the security of mobile devices. Researchers are investigating a variety of cutting-edge approaches, procedures, and strategies to analyse and identify it. The purpose of this literature review is to examine recent research on Android malware analysis, with a particular emphasis on novel methodologies and the degree to which they are successful in identifying and mitigating the threat. This paper reviews the three common approaches and discuss the challenges and limitations identified. Objective: This literature review aims to provide a comprehensive overview of Android malware analysis techniques and methodologies, evaluating the effectiveness of different approaches like static, dynamic, machine learning and deep learning. It also evaluates existing tools and frameworks, highlights recent studies’ contributions and highlights gaps in research. The review aims to enhance detection mitigation strategies for mobile security and provide insights for researchers, practitioners, and policymakers. Design/Methodology/Approach: The SWOT analysis method is used to conduct data analysis and present the results from a variety of sources, including academic papers, web articles, journals, and other sources. Findings/Result: The literature study focuses on several different strategies and methods for analysing Android malware, such as static, dynamic, machine learning, and deep learning. These techniques are used to extract features, analyse code structure, and identify dangerous behaviours. It is essential for effective detection solutions to incorporate a variety of approaches as well as extensive datasets. Most of the research has been directed towards improving machine learning models rather than the malware analysis process.

Key Words

android malware, malware detection, HinDroid, static analysis, API, cyber security

Cite This Article

"ANDROID MALWARE ANALYSIS – A LITERATURE REVIEW", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.e306-e325, January-2024, Available :http://www.jetir.org/papers/JETIR2401437.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

"ANDROID MALWARE ANALYSIS – A LITERATURE REVIEW", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppe306-e325, January-2024, Available at : http://www.jetir.org/papers/JETIR2401437.pdf

Publication Details

Published Paper ID: JETIR2401437
Registration ID: 531699
Published In: Volume 11 | Issue 1 | Year January-2024
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.37623
Page No: e306-e325
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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