Malicious apps Identification in Android Devices Using Machine Learning Algorithms-Reference-Cited by-同舟云学术

Malicious apps Identification in Android Devices Using Machine Learning Algorithms

Published:2020-12-18 Issue:4 Volume:10 Page:559-569
ISSN:2210-3279
Container-title:International Journal of Sensors, Wireless Communications and Control
language:en
Short-container-title:SWCC

Author:

Ahuja Ravinder¹^ORCID,Maheshwari Vineet²^ORCID,Manglik Siddhant²^ORCID,Kazmi Abiha²^ORCID,Arora Rishika²^ORCID,Gupta Anuradha²^ORCID

Affiliation:

1. Electronics and Computer Discipline, Indian Institute of Technology Roorkee, Roorkee, India

2. Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India

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.

Publisher

Bentham Science Publishers Ltd.

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Computer Science Applications

Reference43 articles.

1. Aswini A.M.; Vinod P.; Security, Privacy, and Applied Cryptography Engineering SPACE Lecture Notes in Computer Science,2014

2. Wang W.; Li Y.; Wang X.; Liu J.; Zhang X.; Detecting android malicious apps and categorizing benign apps with an ensemble of classifiers. Future Gener Comput Syst 2018,78,987-994

3. Pandita R.; Xiao X.; Yang W.; Enck W.; Xie T.; WHYPER: Towards automating risk assessment of mobile applications the 22nd USENIX Security Symposium (USENIX Security 13),2013

4. Barrera D.; Kayacik H.G.; van Oorschot P.C.; Somayaji A.; A methodology for empirical analysis of permission-based security models and its application to android. . Proceedings of the17th ACM conference on Computer and communications security,2010

5. Mahindru A.; Singh P.; Dynamic permissions based android malware detection using machine learning techniques. Proceedings of the 10th Innovations in Software Engineering Conference 2017

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