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Security and Communication Networks
Volume 2017 (2017), Article ID 6451260, 14 pages
Research Article

Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

1College of Computer Science and Electronics Engineering, Hunan University, Changsha, China
2Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy, Changsha, China
3Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges, Hunan Police Academy, Changsha, China
4Department of Computer Sciences, New York Institute of Technology, New York, NY, USA

Correspondence should be addressed to Dafang Zhang; nc.ude.unh@gnahzfd

Received 23 January 2017; Revised 4 June 2017; Accepted 6 July 2017; Published 28 August 2017

Academic Editor: Jesús Díaz-Verdejo

Copyright © 2017 Xin Wang et al. 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.


In recent years, Android malware has continued to grow at an alarming rate. More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective. More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm. To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect. To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection. Then, a parallel machine learning detection model is developed for speeding up the process of classification. Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results. To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps. The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions.