NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification
Authors
Year
2024
Published
Computer Networks. 2024, 240 1-22. ISSN 1389-1286.
Type
Article
Departments
Annotation
Network traffic monitoring based on IP Flows is a standard monitoring approach that can be deployed to various network infrastructures, even the large ISP networks connecting millions of people. Since flow records traditionally contain only limited information (addresses, transport ports, and amount of exchanged data), they are also commonly extended by additional features that enable network traffic analysis with high accuracy. These flow extensions are, however, often too large or hard to compute, which then allows only offline analysis or limits their deployment only to smaller-sized networks. This paper proposes a novel extended IP flow called NetTiSA (Network Time Series Analysed) flow, based on analysing the time series of packet sizes. By thoroughly testing 25 different network traffic classification tasks, we show the broad applicability and high usability of NetTiSA flow. For practical deployment, we also consider the sizes of flows extended by NetTiSA features and evaluate the performance impacts of their computation in the flow exporter. The novel features proved to be computationally inexpensive and showed excellent discriminatory performance. The trained machine learning classifiers with proposed features mostly outperformed the state-of-the-art methods. NetTiSA finally bridges the gap and brings universal, small-sized, and computationally inexpensive features for traffic classification that can be scaled up to extensive monitoring infrastructures, bringing the machine learning traffic classification even to 100 Gbps backbone lines.
Network traffic classification based on periodic behavior detection
Authors
Year
2022
Published
Proceedings of 2022 18th International Conference on Network and Service Management (CNSM). New York: IEEE, 2022. p. 359-363. ISSN 2165-9605. ISBN 978-3-903176-51-5.
Type
Proceedings paper
Annotation
Even though encryption hides the content of communication from network monitoring and security systems, this paper shows a feasible way to retrieve useful information about the observed traffic. The paper deals with detection of periodic behavioral patterns of the communication that can be detected using time series created from network traffic by autocorrelation function and Lomb-Scargle periodogram. The revealed characteristics of the periodic behavior can be further exploited to recognize particular applications. We have experimented with the created dataset of 61 classes, and trained a machine learning classifier based on XGBoost that performed the best in our experiments, reaching 90% F1-score.