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，例如管理带宽和确保安全。的 需要带宽管理方法来优化网络性能并提供高质量的 近年来，服务（QoS）保证显著增加。作为新的社交网络和 诸如Facebook和Skype之类的对等（P2P）应用程序已经显著地流行起来 在过去几年中，它们现在在许多网络的总流量中占很大的份额。 因此，这种网络流量的识别在诸如流量管理的许多领域中起着重要作用。 在这项研究中，我们的目标是调查加密的Facebook流量如何 可以通过观察小的分组序列的统计特性来准确和快速地识别 美德.先知-愿的确，覆盖不同加密行为的集合使得区分困难 非Facebook的流量。此外，在加密的隧道中，预测什么是具有挑战性的， 在加密的Facebook流量中使用一种Facebook通信（例如语音、聊天或视频）， 解密流量因此，这项研究的目标是开发一个模型，不仅区分Facebook 流量从网络上的非Facebook流量中分离出来，而且还能够将Facebook流量分离成聊天、语音 和视频组件而不解密。

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.