The availability of open source traffic classification[1][2][3][4][5] systems designed for both experimental and operational use can facilitate cooperation, union on standard definitions and events and trusted evaluation of methods. In this paper, we describe Traffic Identification Engine (TIE), an open source tool for network transfer classification, Investigating the optimal combination strategy and set of classifiers to generate reliable ground truth while preserving privacy Extending the support for sharing labeled traffic with anonym zed traces Investigating strategies for multi-threaded classification, exploiting: off-load techniques focuses by recent traffic capturing engines such as multi queue adapters. Comparing the accuracy of different classifiers and classification performance. Investigating multi-classification and combination strategies.