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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM4450

MALWARE DETECTION USING ML

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM4450

Volume:

4 |

6 |

Jun 2020

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AG

Aniket Gupta

AA

Akashdeep Arora

YA

Yukta Anand

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Abstract

The “Malware Detection on Application using Machine Learning” project is a focused initiative aimed at enhancing the security of mobile applications through advanced detection mechanisms. As the threat landscape for mobile app-based malware continues to evolve, this project leverages the power of machine learning to develop robust and adaptive detection models. The project involves the analysis of application behavior, employing both static and dynamic approaches, to identify and categorize potential malware instances. By training machine learning models on extensive datasets, the project aims to enable the detection of diverse forms of malicious software, including viruses and trojans. The integration of machine learning into the detection process facilitates continuous learning and adaptation to emerging threats.

KEYWORDS

Malware Detection Machine Learning Mobile Applications Security Enhancement Threat Landscape Behavioral Analysis Static Analysis Dynamic Analysis Virus Detection Trojan Detection Adaptive Models Continuous Learning Emerging Threats Proactive Defense Mobile App Security.

Cite This Article

Aniket Gupta, Akashdeep Arora, Yukta Anand, "MALWARE DETECTION USING ML," International Journal of Scientific Research in Engineering and Management, vol. 4, no. 6, Jun 2020.

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Publication Date

12/14/2023

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Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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