KISS: Stochastic Packet Inspection Classifier for UDP Traffic

This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications

Comparing P2PTV Traffic Classifiers

Peer-to-Peer IP Television (P2PTV) applications represent one of the fastest growing application classes on the Internet, both in terms of their popularity and in terms of the amount of traffic they generate. While network operators require monitoring tools that can effectively analyze the traffic produced by these systems, few techniques have been tested on these mostly closed-source, proprietary applications. In this paper we examine the properties of three traffic classifiers applied to the problem of identifying P2PTV traffic. We report on extensive experiments conducted on traffic traces with reliable ground truth information, highlighting the benefits and shortcomings of each approach. The results show that not only their performance in terms of accuracy can vary significantly, but also that their usability features suggest different effective aspects that can be integrate

Characterization of ISP Traffic: Trends, User Habits, and Access Technology Impact

In the recent years, the research community has increased its focus on network monitoring which is seen as a key tool to understand the Internet and the Internet users. Several studies have presented a deep characterization of a particular application, or a particular network, considering the point of view of either the ISP, or the Internet user. In this paper, we take a different perspective. We focus on three European countries where we have been collecting traffic for more than a year and a half through 5 vantage points with different access technologies. This humongous amount of information allows us not only to provide precise, multiple, and quantitative measurements of "What the user do with the Internet" in each country but also to identify common/uncommon patterns and habits across different countries and nations. Considering different time scales, we start presenting the trend of application popularity; then we focus our attention to a one-month long period, and further drill into a typical daily characterization of users activity. Results depict an evolving scenario due to the consolidation of new services as Video Streaming and File Hosting and to the adoption of new P2P technologies. Despite the heterogeneity of the users, some common tendencies emerge that can be leveraged by the ISPs to improve their servic

Mining Unclassified Traffic Using Automatic Clustering Techniques

In this paper we present a fully unsupervised algorithm to identify classes of traffic inside an aggregate. The algorithm leverages on the K-means clustering algorithm, augmented with a mechanism to automatically determine the number of traffic clusters. The signatures used for clustering are statistical representations of the application layer protocols. The proposed technique is extensively tested considering UDP traffic traces collected from operative networks. Performance tests show that it can clusterize the traffic in few tens of pure clusters, achieving an accuracy above 95%. Results are promising and suggest that the proposed approach might effectively be used for automatic traffic monitoring, e.g., to identify the birth of new applications and protocols, or the presence of anomalous or unexpected traffi

Peer-to-Peer Communication Across Network Address Translators

Network Address Translation (NAT) causes well-known difficulties for peer-to-peer (P2P) communication, since the peers involved may not be reachable at any globally valid IP address. Several NAT traversal techniques are known, but their documentation is slim, and data about their robustness or relative merits is slimmer. This paper documents and analyzes one of the simplest but most robust and practical NAT traversal techniques, commonly known as "hole punching." Hole punching is moderately well-understood for UDP communication, but we show how it can be reliably used to set up peer-to-peer TCP streams as well. After gathering data on the reliability of this technique on a wide variety of deployed NATs, we find that about 82% of the NATs tested support hole punching for UDP, and about 64% support hole punching for TCP streams. As NAT vendors become increasingly conscious of the needs of important P2P applications such as Voice over IP and online gaming protocols, support for hole punching is likely to increase in the future.Comment: 8 figures, 1 tabl