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The Time-Varying Dependency Patterns of NetFlow Statistics

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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The Time-Varying Dependency Patterns of NetFlow Statistics. / Gibberd, A.; Evangelou, M.; Nelson, J. D. B.
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. p. 288-294 (2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Gibberd, A, Evangelou, M & Nelson, JDB 2016, The Time-Varying Dependency Patterns of NetFlow Statistics. in 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 288-294. https://doi.org/10.1109/ICDMW.2016.0048

APA

Gibberd, A., Evangelou, M., & Nelson, J. D. B. (2016). The Time-Varying Dependency Patterns of NetFlow Statistics. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) (pp. 288-294). (2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)). IEEE. https://doi.org/10.1109/ICDMW.2016.0048

Vancouver

Gibberd A, Evangelou M, Nelson JDB. The Time-Varying Dependency Patterns of NetFlow Statistics. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE. 2016. p. 288-294. (2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)). doi: 10.1109/ICDMW.2016.0048

Author

Gibberd, A. ; Evangelou, M. ; Nelson, J. D. B. / The Time-Varying Dependency Patterns of NetFlow Statistics. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. pp. 288-294 (2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)).

Bibtex

@inproceedings{f25363b764934c12a200ae7821b7612f,
title = "The Time-Varying Dependency Patterns of NetFlow Statistics",
abstract = "We investigate where and how key dependency structure between measures of network activity change throughout the course of daily activity. Our approach to data-mining is probabilistic in nature, we formulate the identification of dependency patterns as a regularised statistical estimation problem. The resulting model can be interpreted as a set of time-varying graphs and provides a useful visual interpretation of network activity. We believe this is the first application of dynamic graphical modelling to network traffic of this kind. Investigations are performed on 9 days of real-world network traffic across a subset of IP's. We demonstrate that dependency between features may change across time and discuss how these change at an intra and inter-day level. Such variation in feature dependency may have important consequences for the design and implementation of probabilistic intrusion detection systems.",
author = "A. Gibberd and M. Evangelou and Nelson, {J. D. B.}",
note = "{\textcopyright}2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2016",
month = dec,
day = "1",
doi = "10.1109/ICDMW.2016.0048",
language = "English",
isbn = "9781509059119",
series = "2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)",
publisher = "IEEE",
pages = "288--294",
booktitle = "2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)",

}

RIS

TY - GEN

T1 - The Time-Varying Dependency Patterns of NetFlow Statistics

AU - Gibberd, A.

AU - Evangelou, M.

AU - Nelson, J. D. B.

N1 - ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/12/1

Y1 - 2016/12/1

N2 - We investigate where and how key dependency structure between measures of network activity change throughout the course of daily activity. Our approach to data-mining is probabilistic in nature, we formulate the identification of dependency patterns as a regularised statistical estimation problem. The resulting model can be interpreted as a set of time-varying graphs and provides a useful visual interpretation of network activity. We believe this is the first application of dynamic graphical modelling to network traffic of this kind. Investigations are performed on 9 days of real-world network traffic across a subset of IP's. We demonstrate that dependency between features may change across time and discuss how these change at an intra and inter-day level. Such variation in feature dependency may have important consequences for the design and implementation of probabilistic intrusion detection systems.

AB - We investigate where and how key dependency structure between measures of network activity change throughout the course of daily activity. Our approach to data-mining is probabilistic in nature, we formulate the identification of dependency patterns as a regularised statistical estimation problem. The resulting model can be interpreted as a set of time-varying graphs and provides a useful visual interpretation of network activity. We believe this is the first application of dynamic graphical modelling to network traffic of this kind. Investigations are performed on 9 days of real-world network traffic across a subset of IP's. We demonstrate that dependency between features may change across time and discuss how these change at an intra and inter-day level. Such variation in feature dependency may have important consequences for the design and implementation of probabilistic intrusion detection systems.

U2 - 10.1109/ICDMW.2016.0048

DO - 10.1109/ICDMW.2016.0048

M3 - Conference contribution/Paper

SN - 9781509059119

T3 - 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

SP - 288

EP - 294

BT - 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

PB - IEEE

ER -