The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l (2005)
Nov. 15, 2005 to Nov. 17, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/LCN.2005.35
Sebastian Zander , Swinburne University of Technology, Melbourne
Thuy Nguyen , Swinburne University of Technology, Melbourne
Grenville Armitage , Swinburne University of Technology, Melbourne
<p>The dynamic classification and identification of network applications responsible for network traffic flows offers substantial benefits to a number of key areas in IP network engineering, management and surveillance. Currently such classifications rely on selected packet header fields (e.g. port numbers) or application layer protocol decoding. These methods have a number of shortfalls e.g. many applications can use unpredictable port numbers and protocol decoding requires a high amount of computing resources or is simply infeasible in case protocols are unknown or encrypted. We propose a novel method for traffic classification and application identification using an unsupervised machine learning technique. Flows are automatically classified based on statistical flow characteristics. We evaluate the efficiency of our approach using data from several traffic traces collected at different locations of the Internet. We use feature selection to find an optimal feature set and determine the influence of different features.</p>
S. Zander, T. Nguyen and G. Armitage, "Automated Traffic Classification and Application Identification using Machine Learning," The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l(LCN), Sydney, Australia, 2005, pp. 250-257.