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Adaptive Mobile Malware Detection Model Based on CBR

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Adaptive Mobile Malware Detection Model Based on CBR

Kyaw Soe Moe | Mya Mya Thwe

Kyaw Soe Moe | Mya Mya Thwe "Adaptive Mobile Malware Detection Model Based on CBR" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6, October 2019, pp.231-238, URL: https://www.ijtsrd.com/papers/ijtsrd28088.pdf

Today, the mobile phones can maintain lots of sensitive information. With the increasing capabilities of such phones, more and more malicious software (malware) targeting these devices have emerged. However there are many mobile malware detection techniques, they used specified classifiers on selected features to get their best accuracy. Thus, an adaptive malware detection approach is required to effectively detect the concept drift of mobile malware and maintain the accuracy. An adaptive malware detection approach is proposed based on case-based reasoning technique in this paper to handle the concept drift issue in mobile malware detection. To demonstrate the design decision of our approach, several experiments are conducted. Large features set with 1,065 features from 10 different categories are used in evaluation. The evaluation includes both accuracy and efficiency of the model. The experimental results prove that our approach achieves acceptable performance and accuracy for the malware detection.

mobile security; machine learning; concept drift; cyber security; data mining

Volume-3 | Issue-6, October 2019
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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