Analyzing Encrypted Facebook Traffic

准确的网络流量识别将有助于网络运营和管理团队 有效地执行许多不同的网络任务，如管理带宽和确保安全。这个 对带宽管理方法的需求，这些方法可优化网络性能并提供 近年来，服务(Qos)保证大幅增加。作为新的社交网络和 Facebook和Skype等点对点(P2P)应用程序的受欢迎程度大幅上升 在过去的几年里，它们现在在许多网络的总流量中占据了相当大的份额。 因此，此类网络流量的识别在流量等多个领域中发挥着重要作用 工程、服务质量、安全等。在这项研究中，我们旨在调查加密的Facebook流量是如何 可以通过观察小数据包序列的统计属性来准确和快速地识别 属性。事实上，覆盖一组不同的加密行为很难区分 来自非Facebook流量的Facebook。此外，身处加密隧道中，很难预测 在加密的Facebook流量中使用一种Facebook通信(例如语音、聊天或视频)，而不是 对流量进行解密。因此，这项研究的目标是开发一种模型，不仅区别于Facebook 流量来自网络上的非Facebook流量，但也能够将Facebook流量分离为聊天、语音 和视频分量而不对其解密。

Accurate network traffic identification would assist network operations and management teams effectively on many different network tasks such as managing bandwidth and ensuring security. The demand for bandwidth management methods that optimize network performance and provide quality of service (QoS) guarantees has increased substantially in recent years. As new social networking and peer-to-peer (P2P) applications such as Facebook and Skype have dramatically grown in popularity over the past few years, they now constitute a significant share of the total traffic in many networks. Therefore, identification of such network traffic plays an important role in many areas such as traffic engineering, QoS, security and so on. In this research, we aim to investigate how encrypted Facebook traffic can be accurately and rapidly identified from observing the statistical properties of a small sequence of packet attributes. Indeed, covering a collection of different encrypted behaviors makes it difficult to distinguish Facebook from non-Facebook traffic. Moreover, being in encrypted tunnels, it is challenging to predict what kind of a Facebook communication (e.g. voice, chat or video) is used in the encrypted Facebook traffic without decrypting the traffic. Thus, the goal of this research is to develop a model that not only distinguishes Facebook traffic from non-Facebook traffic on the network, but also is able to separate Facebook traffic into chat, voice and video components without decrypting it.