Year: 2005
Type: conference-paper
Abstract: 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
DOI: 10.1109/lcn.2005.35
Authors: S. Zander, T. Nguyen, G. Armitage
Institution: Swinburne University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2149274049