We present two sets of results: 1) Internet measurements of keyword filtering by the Great 'Firewall' of China (GFC) and 2) initial results of using la- tent semantic analysis as an efficient way to reproduce a blacklist of censored words via probing. The text of this paper has passed across many Internet routers on its way to the reader, but some routers will not pass it along unfet- tered because of censored words it contains.

Although still preliminary, experimental results show that the technique can be quite effective, and that the standard bodies might need to take this approach under consideration when designing new obfuscation techniques for SSH. This technique can therefore break the presumption of privacy in its second incarnation as described above. In this paper we introduce a GMM-based (Gaussian Mixture Model) technique that, under a set of reasonable assumptions, can be used to identify which application is being tunneled inside an SSH session by simply observing the stream of encrypted packets. Second, they are supposed to protect the privacy of the behavior of end-users, by preventing an unauthorized observer from detecting which application protocol is being transported by an SSH tunnel. First and foremost, they aim at protecting the privacy of the data being exchanged between two peers, such as passwords, details of monetary transactions and so on. Secure Shell (SSH) tunnels are commonly used to provide two types of privacy protection to clear-text application protocols. Results show how the proposed approach is able to classify network traffic by using packet-level statistical properties and therefore it is a good candidate as a component for a multi-classification framework. An analytical basis and the mathematical details regarding the model are given. The effectiveness of the proposed approach is evaluated by considering several traffic typologies: we applied our model to real traffic traces of Age of Mythology and Counter Strike (two Multi Player Network Games), HTTP, SMTP, Edonkey, PPlive (a peer-to-peer IPTV application), and MSN Messenger. Classification is performed by using real network traffic and estimating - in a combined fashion - Packet Size (PS) and Inter Packet Time (IPT) characteristics, thus remaining applicable to encrypted traffic too. This paper proposes a packet-level traffic classification approach based on Hidden Markov Model (HMM). Beyond Quality of Service, service differentiation, and billing, one of the most important applications of traffic classification is in the field of network security. Traffic classification and identification is a fertile research area.

Tor messenger ident