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Anomaly detection in the cloud using data density

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Anomaly detection in the cloud using data density. / Shirazi, Syed Noor Ul Hassan ; Simpson, Steven; Gouglidis, Antonios et al.

Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on. IEEE, 2016. p. 616-623 7820324 (Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on).

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

Harvard

Shirazi, SNUH, Simpson, S, Gouglidis, A, Mauthe, AU & Hutchison, D 2016, Anomaly detection in the cloud using data density. in Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on., 7820324, Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on, IEEE, pp. 616-623, 9th International Conference on Cloud Computing, CLOUD 2016, San Francisco, United States, 27/06/16. https://doi.org/10.1109/CLOUD.2016.0087, https://doi.org/10.1109/CLOUD.2016.85

APA

Shirazi, S. N. U. H., Simpson, S., Gouglidis, A., Mauthe, A. U., & Hutchison, D. (2016). Anomaly detection in the cloud using data density. In Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on (pp. 616-623). [7820324] (Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on). IEEE. https://doi.org/10.1109/CLOUD.2016.0087, https://doi.org/10.1109/CLOUD.2016.85

Vancouver

Shirazi SNUH, Simpson S, Gouglidis A, Mauthe AU, Hutchison D. Anomaly detection in the cloud using data density. In Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on. IEEE. 2016. p. 616-623. 7820324. (Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on). doi: 10.1109/CLOUD.2016.0087, 10.1109/CLOUD.2016.85

Author

Shirazi, Syed Noor Ul Hassan ; Simpson, Steven ; Gouglidis, Antonios et al. / Anomaly detection in the cloud using data density. Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on. IEEE, 2016. pp. 616-623 (Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on).

Bibtex

@inproceedings{1753954f084f4342b904b08302588bcf,
title = "Anomaly detection in the cloud using data density",
abstract = "Cloud computing is now extremely popular because of its use of elastic resources to provide optimized, cost-effective and on-demand services. However, clouds may be subject to challenges arising from cyber attacks including DoS and malware, as well as from sheer complexity problems that manifest themselves as anomalies. Anomaly detection techniques are used increasingly to improve the resilience of cloud environments and indirectly reduce the cost of recovery from outages. Most anomaly detection techniques are computation ally expensive in a cloud context, and often require problem-specific parameters to be predefined in advance, impairing their use in real-time detection. Aiming to overcome these problems, we propose a technique for anomaly detection based on data density. The density is computed recursively, so the technique is memory-less and unsupervised, and therefore suitable for real-time cloud environments. We demonstrate the efficacy of the proposed technique using an emulated dataset from a testbed,under various attack types and intensities, and in the face of VM migration. The obtained results, which include precision, recall, accuracy, F-score and G-score, show that network level attacks are detectable with high accuracy.",
author = "Shirazi, {Syed Noor Ul Hassan} and Steven Simpson and Antonios Gouglidis and Mauthe, {Andreas Ulrich} and David Hutchison",
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.; 9th International Conference on Cloud Computing, CLOUD 2016 ; Conference date: 27-06-2016 Through 02-07-2016",
year = "2016",
month = jun,
day = "27",
doi = "10.1109/CLOUD.2016.0087",
language = "English",
isbn = "9781509026203",
series = "Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on",
publisher = "IEEE",
pages = "616--623",
booktitle = "Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on",

}

RIS

TY - GEN

T1 - Anomaly detection in the cloud using data density

AU - Shirazi, Syed Noor Ul Hassan

AU - Simpson, Steven

AU - Gouglidis, Antonios

AU - Mauthe, Andreas Ulrich

AU - Hutchison, David

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 - 2016/6/27

Y1 - 2016/6/27

N2 - Cloud computing is now extremely popular because of its use of elastic resources to provide optimized, cost-effective and on-demand services. However, clouds may be subject to challenges arising from cyber attacks including DoS and malware, as well as from sheer complexity problems that manifest themselves as anomalies. Anomaly detection techniques are used increasingly to improve the resilience of cloud environments and indirectly reduce the cost of recovery from outages. Most anomaly detection techniques are computation ally expensive in a cloud context, and often require problem-specific parameters to be predefined in advance, impairing their use in real-time detection. Aiming to overcome these problems, we propose a technique for anomaly detection based on data density. The density is computed recursively, so the technique is memory-less and unsupervised, and therefore suitable for real-time cloud environments. We demonstrate the efficacy of the proposed technique using an emulated dataset from a testbed,under various attack types and intensities, and in the face of VM migration. The obtained results, which include precision, recall, accuracy, F-score and G-score, show that network level attacks are detectable with high accuracy.

AB - Cloud computing is now extremely popular because of its use of elastic resources to provide optimized, cost-effective and on-demand services. However, clouds may be subject to challenges arising from cyber attacks including DoS and malware, as well as from sheer complexity problems that manifest themselves as anomalies. Anomaly detection techniques are used increasingly to improve the resilience of cloud environments and indirectly reduce the cost of recovery from outages. Most anomaly detection techniques are computation ally expensive in a cloud context, and often require problem-specific parameters to be predefined in advance, impairing their use in real-time detection. Aiming to overcome these problems, we propose a technique for anomaly detection based on data density. The density is computed recursively, so the technique is memory-less and unsupervised, and therefore suitable for real-time cloud environments. We demonstrate the efficacy of the proposed technique using an emulated dataset from a testbed,under various attack types and intensities, and in the face of VM migration. The obtained results, which include precision, recall, accuracy, F-score and G-score, show that network level attacks are detectable with high accuracy.

U2 - 10.1109/CLOUD.2016.0087

DO - 10.1109/CLOUD.2016.0087

M3 - Conference contribution/Paper

AN - SCOPUS:85014161135

SN - 9781509026203

T3 - Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on

SP - 616

EP - 623

BT - Cloud Computing (CLOUD), 2016 IEEE 9th International Conference on

PB - IEEE

T2 - 9th International Conference on Cloud Computing, CLOUD 2016

Y2 - 27 June 2016 through 2 July 2016

ER -