Traffic analysis

Abstract: Onion routing is an infrastructure for private communication over a public network. It provides anonymous connections that are strongly resistant to both eavesdropping and traffic analysis. Onion routing's anonymous connections are bidirectional, near real-time, and can be used anywhere a socket connection can be used. Any identifying information must be in the data stream carried over an anonymous connection. An onion is a data structure that is treated as the destination address by onion routers; thus, it is used to establish an anonymous connection. Onions themselves appear different to each onion router as well as to network observers. The same goes for data carried over the connections they establish. Proxy-aware applications, such as Web browsers and e-mail clients, require no modification to use onion routing, and do so through a series of proxies. A prototype onion routing network is running between our lab and other sites. This paper describes anonymous connections and their implementation using onion routing. This paper also describes several application proxies for onion routing, as well as configurations of onion routing networks.

Abstract: The ability to accurately identify the network traffic associated with different P2P applications is important to a broad range of network operations including application-specific traffic engineering, capacity planning, provisioning, service differentiation,etc. However, traditional traffic to higher-level application mapping techniques such as default server TCP or UDP network-port baseddisambiguation is highly inaccurate for some P2P applications.In this paper, we provide an efficient approach for identifying the P2P application traffic through application level signatures. We firstidentify the application level signatures by examining some available documentations, and packet-level traces. We then utilize the identified signatures to develop online filters that can efficiently and accurately track the P2P traffic even on high-speed network links.We examine the performance of our application-level identification approach using five popular P2P protocols. Our measurements show thatour technique achieves less than 5% false positive and false negative ratios in most cases. We also show that our approach only requires the examination of the very first few packets (less than 10packets) to identify a P2P connection, which makes our approach highly scalable. Our technique can significantly improve the P2P traffic volume estimates over what pure network port based approaches provide. For instance, we were able to identify 3 times as much traffic for the popular Kazaa P2P protocol, compared to the traditional port-based approach.

Abstract: Onion routing provides anonymous connections that are strongly resistant to both eavesdropping and traffic analysis. Unmodified Internet applications can use these anonymous connections by means of proxies. The proxies may also make communication anonymous by removing identifying information from the data stream. Onion routing has been implemented on Sun Solaris 2.X with proxies for Web browsing, remote logins and e-mail. This paper's contribution is a detailed specification of the implemented onion routing system, a vulnerability analysis based on this specification, and performance results.

Abstract: Automatic video analysis from urban surveillance cameras is a fast-emerging field based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing deployment of cameras have opened a wide application field for video analytics. Several monitoring objectives such as congestion, traffic rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human operators. Systems for the detection and classification of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to traffic density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct comparison of the proposed algorithms difficult. In addition, evaluation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classifiers for all road users, with a focus on realistic conditions during evaluation.