Abstract. Packet header traces are widely used in network analysis. Header traces are the aggregate of traffic from many concurrent applications. We present a methodology, based on machine learning, that can break the trace down into clusters of traffic where each cluster has different traffic characteristics. Typical clusters include bulk transfer, single and multiple transactions and interactive traffic, amongst others. The paper includes a description of the methodology, a visualisation of the attribute statistics that aids in recognising cluster types and a discussion of the stability and effectiveness of the methodology.

This paper introduces a new set of long duration captures of Internet traffic headers. The capture is being performed on a continuous on-going basis and is approaching a year in duration. Based on the current extent of the archive some typical analyses are presented, covering protocol mix, network trip times and TCP flag analysis.

a c m s i g c o m m ABSTRACTThis paper introduces libtrace, an open-source software library for reading and writing network packet traces. Libtrace offers performance and usability enhancements compared to other libraries that are currently used. We describe the main features of libtrace and demonstrate how the libtrace programming API enables users to easily develop portable trace analysis tools without needing to consider the details of the capture format, file compression or intermediate protocol headers. We compare the performance of libtrace against other trace processing libraries to show that libtrace offers the best compromise between development effort and program run time. As a result, we conclude that libtrace is a valuable contribution to the passive measurement community that will aid the development of better and more reliable trace analysis and network monitoring tools.