Malicious apps Identification in Android Devices Using Machine Learning Algorithms

Author(s): Ravinder Ahuja*, Vineet Maheshwari, Siddhant Manglik, Abiha Kazmi, Rishika Arora, Anuradha Gupta

Journal Name: International Journal of Sensors, Wireless Communications and Control

Volume 10 , Issue 4 , 2020


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Graphical Abstract:


Abstract:

Background & Objective: In this paper, malicious apps detection system is implemented using machine learning algorithms. For this 330 permission based features of 558 android applications are taken into consideration.

Methods: The main motto of this work is to develop a model which can effectively detect the malicious and benign apps. In this we have used six feature selection techniques which will extract important features from 330 permission based features of 558 apps and further fourteen classification algorithms are applied using Python language.

Results: In this paper, an efficient model for detecting malicious apps has been proposed.

Conclusion: Proposed model is able to detect malicious apps approx. 3% better than existing system.

Keywords: Classification techniques, feature selection, malware identification, ensemble algorithms, static analysis, python language.

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Article Details

VOLUME: 10
ISSUE: 4
Year: 2020
Published on: 18 December, 2020
Page: [559 - 569]
Pages: 11
DOI: 10.2174/2210327909666191204125100
Price: $25

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