Security Evaluation Research of Mobile Payment Application Based on SVM

DOI

10.2991/icca-16.2016.113How to use a DOI?

Keywords

Support vector machine; Kernel function; Feature weights

Abstract

With the increase of mobile payment malicious applications, an accurate assessment of their safety is particularly important. In recent years, machine learning methods have achieved good results in text recognition, medical diagnosis, anomaly detection and other fields. Therefore, we consider the introduction of the application of many features including application signature information, application permissions, suspicious API, special string to construct a sample feature space and evaluate the safety of mobile payment applications based on support vector machine algorithm. By comparing the accuracy of a variety of algorithms, the algorithm is turned to be feasible. The importance of the evaluation index is different during the sample classification and there is a difference in the performance of the kernel function under different conditions. So this paper studies the performance of the classifier in different evaluation index set, kernel function and feature weights. It proves that the polynomial kernel support vector machine method is best based on feature weights after the introduction of four types of evaluation index. This method properly avoids the problem of low accuracy due to a single feature while eliminating the adverse effects of weak correlation evaluation index.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yang Liu
AU  - Juan Xu
AU  - Hui Fei
AU  - Yanhui Guo
AU  - Guoai Xu
PY  - 2016/01
DA  - 2016/01
TI  - Security Evaluation Research of Mobile Payment Application Based on SVM
BT  - Proceedings of the 2016 International Conference on Intelligent Control and Computer Application
PB  - Atlantis Press
SP  - 477
EP  - 483
SN  - 2352-538X
UR  - https://doi.org/10.2991/icca-16.2016.113
DO  - 10.2991/icca-16.2016.113
ID  - Liu2016/01
ER  -