Encrypted social media traffic classification

网络流量数据包携带属于各种应用的数据。流量分类可以帮助网络运营商识别网络中存在的特定应用程序和协议，这些应用程序和协议可用于许多不同的目的，如网络规划、用于服务质量保证的应用程序优先级划分和用于安全控制的策略部署。网络流量监控正在成为网络安全中的必要手段。脸书、推特、WhatsApp、Snapchat、Instagram、微博和微信等社交媒体的数据包都是加密的。因此，分析和分类是一项挑战。社交网络涵盖了各种类型的数据，如视频、文本、图片和视频，分类时应考虑这些数据。互联网协议安全(IPSec)、TLS、SSH、Bit Torrent和Skype协议等加密协议也受到加密流量分类研究中最常用的协议的影响。几乎所有现有的加密协议都可以分为两个主要阶段：连接的初始化和加密数据的传输。本项目的目标是开发用于社交媒体加密流量分类的机器学习算法。该项目将汇集瑞尔森大学和Solana Networks的人才，并利用Ryerson的资源，包括Solana Networks的经验丰富的研究人员、精力充沛和有目标驱动的毕业生，设计和开发基于机器学习的分类器，共同推进加密社交媒体流量的分类和分析。

Network traffic packets carry data belonging to a variety of applications. Classification of traffic helps networkoperators to identify specific applications and protocols that exist in a network, which can be useful for manydifferent purposes such as network planning, application prioritization for QoS guarantees, and policydeployment for security control.Network traffic monitoring is becoming a necessity in network security. The packets of social media suchas Facebook, Twitter, WhatsApp, Snapchat, Instagram, Weibo and WeChat are encrypted. Therefore, analyzingand classification is a challenge. The social networks cover various types of data such as video, text, picturewhich should be considered in classification. Encryption protocols such as Internet Protocol Security (IPsec),TLS, SSH, Bit Torrent, and Skype protocols was also influenced by protocols that are the most commonly usedin research of encrypted traffic classification. Almost all the presented encryption protocols can be divided intotwo main phases: the initialization of the connection and the transport of encrypted data.The objective of this project is to develop machine learning algorithms for classification of social media inencrypted traffic.This project will pull together talents from Ryerson University and Solana Networks and take theadvantage of the resources of Ryerson including experienced researchers, energetic and goal-driven graduatestudents with a track record of excellence of Solana Networks in design and development of the machinelearning based classifier to jointly advance encrypted social media traffic classification and analysis.