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/).
Cite this article
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 -