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Security and Communication Networks - Volume 2017 2017, Article ID 3284080, 14 pages -

Research Article

School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China

School of Information Technology and Network Security, People’s Public Security University of China, Beijing 100038, China

Correspondence should be addressed to Yanping Xu

Received 10 February 2017; Revised 7 April 2017; Accepted 27 April 2017; Published 17 May 2017

Academic Editor: Pedro Peris-Lopez

Copyright © 2017 Yanping Xu 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.


Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine SVM method based on feature weights that are computed by information gain IG and particle swarm optimization PSO algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness the performance of the SVM classification model. Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO FCAIW-PSO that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights.

Autor: Yanping Xu, Chunhua Wu, Kangfeng Zheng, Xu Wang, Xinxin Niu, and Tianliang Lu



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