Multiple methods for wechat identification

DOI

10.2991/icadme-16.2016.104How to use a DOI?

Keywords

Traffic classification; Wechat identification; Malware detection; text classification; image classification

Abstract

Wechat is a popular social platform developed in 2011 by Tencent. In this paper wechat traffic is analyzed by a novel hybrid method, which combines statistical method, payload-based method, SVM, CRC and deep learning. Firstly, the statistical method is utilized to extract features from wechat packets header, which can classify different wechat applications and functions, e.g. texts, images and voice, and so on. Secondly, payload-based method is used to identify the traffic, which is corresponding to the above functions and application protocols. Thirdly, SVM is applied to categorize the texts based on their attributes. CRC method is used to classify the images, which effectively protects the user's privacy. Finally, deep learning is presented to extract features of wechat app in order to check the malicious software. Experimental results show that, the proposed method has high accuracy for wechat traffic. It not only identifies wechat app, but also detects the specific functions of app. It even discriminates texts, images, voice and malicious software effectively.

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/).

Download article (PDF)

TY  - CONF
AU  - Chunwei Tian
AU  - Qi Zhang
AU  - Guanglu Sun
PY  - 2017/07
DA  - 2017/07
TI  - Multiple methods for wechat identification
BT  - Proceedings of the 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017)
PB  - Atlantis Press
SP  - 598
EP  - 601
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-16.2016.104
DO  - 10.2991/icadme-16.2016.104
ID  - Tian2017/07
ER  -