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Characterising Dependency in Computer Networks using Spectral Coherence

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Characterising Dependency in Computer Networks using Spectral Coherence. / Gibberd, Alex; Noble, Jordan; Cohen, Edward.
Proceedings of the International Conference on Time Series and Forecasting. ITISE, 2018. p. 1147-1157.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Gibberd, A, Noble, J & Cohen, E 2018, Characterising Dependency in Computer Networks using Spectral Coherence. in Proceedings of the International Conference on Time Series and Forecasting. ITISE, pp. 1147-1157, International Conference on Time Series and Forecasting, Granada, Spain, 19/09/18.

APA

Gibberd, A., Noble, J., & Cohen, E. (2018). Characterising Dependency in Computer Networks using Spectral Coherence. In Proceedings of the International Conference on Time Series and Forecasting (pp. 1147-1157). ITISE.

Vancouver

Gibberd A, Noble J, Cohen E. Characterising Dependency in Computer Networks using Spectral Coherence. In Proceedings of the International Conference on Time Series and Forecasting. ITISE. 2018. p. 1147-1157

Author

Gibberd, Alex ; Noble, Jordan ; Cohen, Edward. / Characterising Dependency in Computer Networks using Spectral Coherence. Proceedings of the International Conference on Time Series and Forecasting. ITISE, 2018. pp. 1147-1157

Bibtex

@inproceedings{52201412b75a4baca9765b4036b97fb3,
title = "Characterising Dependency in Computer Networks using Spectral Coherence",
abstract = " The quantification of normal and anomalous traffic flows across computer networks is a topic of pervasive interest in network se- curity, and requires the timely application of time-series methods. The transmission or reception of packets passing between computers can be represented in terms of time-stamped events and the resulting activity understood in terms of point-processes. Interestingly, in the disparate do- main of neuroscience, models for describing dependent point-processes are well developed. In particular, spectral methods which decompose second-order dependency across different frequencies allow for a rich characterisation of point-processes. In this paper, we investigate using the spectral coherence statistic to characterise computer network activ- ity, and determine if, and how, device messaging may be dependent. We demonstrate on real data, that for many devices there appears to be very little dependency between device messaging channels. However, when sig- nificant coherence is detected it appears highly structured, a result which suggests coherence may prove useful for discriminating between types of activity at the network level.",
author = "Alex Gibberd and Jordan Noble and Edward Cohen",
year = "2018",
month = sep,
day = "19",
language = "English",
isbn = "9788417293574",
pages = "1147--1157",
booktitle = "Proceedings of the International Conference on Time Series and Forecasting",
publisher = "ITISE",
note = "International Conference on Time Series and Forecasting, ITSE 2018 ; Conference date: 19-09-2018 Through 21-09-2018",
url = "http://itise.ugr.es/",

}

RIS

TY - GEN

T1 - Characterising Dependency in Computer Networks using Spectral Coherence

AU - Gibberd, Alex

AU - Noble, Jordan

AU - Cohen, Edward

PY - 2018/9/19

Y1 - 2018/9/19

N2 - The quantification of normal and anomalous traffic flows across computer networks is a topic of pervasive interest in network se- curity, and requires the timely application of time-series methods. The transmission or reception of packets passing between computers can be represented in terms of time-stamped events and the resulting activity understood in terms of point-processes. Interestingly, in the disparate do- main of neuroscience, models for describing dependent point-processes are well developed. In particular, spectral methods which decompose second-order dependency across different frequencies allow for a rich characterisation of point-processes. In this paper, we investigate using the spectral coherence statistic to characterise computer network activ- ity, and determine if, and how, device messaging may be dependent. We demonstrate on real data, that for many devices there appears to be very little dependency between device messaging channels. However, when sig- nificant coherence is detected it appears highly structured, a result which suggests coherence may prove useful for discriminating between types of activity at the network level.

AB - The quantification of normal and anomalous traffic flows across computer networks is a topic of pervasive interest in network se- curity, and requires the timely application of time-series methods. The transmission or reception of packets passing between computers can be represented in terms of time-stamped events and the resulting activity understood in terms of point-processes. Interestingly, in the disparate do- main of neuroscience, models for describing dependent point-processes are well developed. In particular, spectral methods which decompose second-order dependency across different frequencies allow for a rich characterisation of point-processes. In this paper, we investigate using the spectral coherence statistic to characterise computer network activ- ity, and determine if, and how, device messaging may be dependent. We demonstrate on real data, that for many devices there appears to be very little dependency between device messaging channels. However, when sig- nificant coherence is detected it appears highly structured, a result which suggests coherence may prove useful for discriminating between types of activity at the network level.

M3 - Conference contribution/Paper

SN - 9788417293574

SP - 1147

EP - 1157

BT - Proceedings of the International Conference on Time Series and Forecasting

PB - ITISE

T2 - International Conference on Time Series and Forecasting

Y2 - 19 September 2018 through 21 September 2018

ER -