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Appl. Sci. 2019, 9(2), 277; https://doi.org/10.3390/app9020277

Research on Data Mining of Permission-Induced Risk for Android IoT Devices

1
School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
School of Electronic Science and & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Received: 25 December 2018 / Revised: 9 January 2019 / Accepted: 9 January 2019 / Published: 14 January 2019
(This article belongs to the Section Computer Science and Electrical Engineering)
Full-Text   |   PDF [3183 KB, uploaded 14 January 2019]   |  

Abstract

With the growing era of the Internet of Things (IoT), more and more devices are connecting with the Internet using android applications to provide various services. The IoT devices are used for sensing, controlling and monitoring of different processes. Most of IoT devices use Android applications for communication and data exchange. Therefore, a secure Android permission privileged mechanism is required to increase the security of apps. According to a recent study, a malicious Android application is developed almost every 10 s. To resist this serious malware campaign, we need effective malware detection approaches to identify malware applications effectively and efficiently. Most of the studies focused on detecting malware based on static and dynamic analysis of the applications. However, to analyse the risky permission at runtime is a challenging task. In this study, first, we proposed a novel approach to distinguish between malware and benign applications based on permission ranking, similarity-based permission feature selection, and association rule for permission mining. Secondly, the proposed methodology also includes the enhancement of the random forest algorithm to improve the accuracy for malware detection. The experimental outcomes demonstrate high proficiency of the accuracy for malware detection, which is pivotal for android apps aiming for secure data exchange between IoT devices. View Full-Text
Keywords: access control; Android malware detection; computer security; permission pattern; secure machine learning access control; Android malware detection; computer security; permission pattern; secure machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kumar, R.; Zhang, X.; Khan, R.U.; Sharif, A. Research on Data Mining of Permission-Induced Risk for Android IoT Devices. Appl. Sci. 2019, 9, 277.

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