The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Structural analysis of network traffic flows
Open accessAbstract
Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties.We present the first analysis of complete sets of OD flow time-series, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions.We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constitutents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.
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An Overview of IP Flow-Based Intrusion Detection
Sperotto, A., Schaffrath, G., Sadre, R., Morariu, C., Pras, A., & Stiller, B. (2010) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2010.032210.00054 · Cited by 466
A comprehensive survey on network anomaly detection
Fernandes, G., Rodrigues, J. J. P. C., Carvalho, L. F., Al-Muhtadi, J. F., & Proença, M. L. (2019) · Telecommunication Systems · doi:10.1007/s11235-018-0475-8 · Cited by 382
Sensitivity of PCA for traffic anomaly detection
Ringberg, H., Soule, A., Rexford, J., & Diot, C. (2007) · Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems · doi:10.1145/1254882.1254895 · Cited by 353
Self-citationMultivariate Online Anomaly Detection Using Kernel Recursive Least Squares
Ahmed, T., Coates, M., & Lakhina, A. (2007) · IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications · doi:10.1109/infcom.2007.79 · Cited by 148
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Metadata
- Authors
- Lakhina, A., Papagiannaki, K., Crovella, M., Diot, C., Kolaczyk, E. D., & Taft, N.
- Venue
- Proceedings of the joint international conference on Measurement and modeling of computer systems
- Publisher
- ACM
- Published
- 2004-06
- Pages
- 61-72
- OpenAlex
- W1997299558
- Cited by
- 451
- References
- 27
- FWCI
- 10.79
- Type
- conference paper
- Language
- en
- Open access
- green
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- Lakhina, A., Papagiannaki, K., Crovella, M., Diot, C., Kolaczyk, E. D., & Taft, N. (2004). Structural analysis of network traffic flows. Proceedings of the joint international conference on Measurement and modeling of computer systems. https://doi.org/10.1145/1005686.1005697
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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