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Tool support for the evaluation of anomaly traffic classification for network resilience

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

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Tool support for the evaluation of anomaly traffic classification for network resilience. / da Silva, Anderson; Wickboldt, Juliano; Schaeffer-Filho, Alberto et al.
Proceedings of 20th IEEE Symposium on Computers and Communications, ISCC2015. IEEE, 2015. p. 514-519.

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

Harvard

da Silva, A, Wickboldt, J, Schaeffer-Filho, A, Marnerides, A & Mauthe, AU 2015, Tool support for the evaluation of anomaly traffic classification for network resilience. in Proceedings of 20th IEEE Symposium on Computers and Communications, ISCC2015. IEEE, pp. 514-519. https://doi.org/10.1109/ISCC.2015.7405566

APA

da Silva, A., Wickboldt, J., Schaeffer-Filho, A., Marnerides, A., & Mauthe, A. U. (2015). Tool support for the evaluation of anomaly traffic classification for network resilience. In Proceedings of 20th IEEE Symposium on Computers and Communications, ISCC2015 (pp. 514-519). IEEE. https://doi.org/10.1109/ISCC.2015.7405566

Vancouver

da Silva A, Wickboldt J, Schaeffer-Filho A, Marnerides A, Mauthe AU. Tool support for the evaluation of anomaly traffic classification for network resilience. In Proceedings of 20th IEEE Symposium on Computers and Communications, ISCC2015. IEEE. 2015. p. 514-519 doi: 10.1109/ISCC.2015.7405566

Author

da Silva, Anderson ; Wickboldt, Juliano ; Schaeffer-Filho, Alberto et al. / Tool support for the evaluation of anomaly traffic classification for network resilience. Proceedings of 20th IEEE Symposium on Computers and Communications, ISCC2015. IEEE, 2015. pp. 514-519

Bibtex

@inproceedings{d89559887a8a4d51836cad42c3330472,
title = "Tool support for the evaluation of anomaly traffic classification for network resilience",
abstract = "Resilience is the ability of the network to maintain an acceptable level of operation in the face of anomalies, such as malicious attacks, operational overload or misconfigurations. Techniques for anomaly traffic classification are often used to characterize suspicious network traffic, thus supporting anomaly detection schemes in network resilience strategies. In this paper, we extend the PReSET toolset to allow the investigation, comparison and analysis of algorithms for anomaly traffic classification based on machine learning. PReSET was designed to allow the simulation-based evaluation of resilience strategies, thus enabling the comparison of optimal configurations and policies for combating different types of attacks (e.g., DDoS attacks, worms) and other anomalies. In such resilience strategies, policies written in the Ponder2 language can be used to activate/reconfigure traffic classification modules and other mechanisms (e.g., traffic shaping), depending on monitored results in the simulation environment. Our results show that PReSET can be a valuable tool for network operators to evaluate anomaly traffic classification techniques in terms of standard performance metrics.",
author = "{da Silva}, Anderson and Juliano Wickboldt and Alberto Schaeffer-Filho and Angelos Marnerides and Mauthe, {Andreas Ulrich}",
note = "{\textcopyright}2015 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 = "2015",
month = jul,
day = "6",
doi = "10.1109/ISCC.2015.7405566",
language = "English",
isbn = "9781467371957",
pages = "514--519",
booktitle = "Proceedings of 20th IEEE Symposium on Computers and Communications, ISCC2015",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Tool support for the evaluation of anomaly traffic classification for network resilience

AU - da Silva, Anderson

AU - Wickboldt, Juliano

AU - Schaeffer-Filho, Alberto

AU - Marnerides, Angelos

AU - Mauthe, Andreas Ulrich

N1 - ©2015 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 - 2015/7/6

Y1 - 2015/7/6

N2 - Resilience is the ability of the network to maintain an acceptable level of operation in the face of anomalies, such as malicious attacks, operational overload or misconfigurations. Techniques for anomaly traffic classification are often used to characterize suspicious network traffic, thus supporting anomaly detection schemes in network resilience strategies. In this paper, we extend the PReSET toolset to allow the investigation, comparison and analysis of algorithms for anomaly traffic classification based on machine learning. PReSET was designed to allow the simulation-based evaluation of resilience strategies, thus enabling the comparison of optimal configurations and policies for combating different types of attacks (e.g., DDoS attacks, worms) and other anomalies. In such resilience strategies, policies written in the Ponder2 language can be used to activate/reconfigure traffic classification modules and other mechanisms (e.g., traffic shaping), depending on monitored results in the simulation environment. Our results show that PReSET can be a valuable tool for network operators to evaluate anomaly traffic classification techniques in terms of standard performance metrics.

AB - Resilience is the ability of the network to maintain an acceptable level of operation in the face of anomalies, such as malicious attacks, operational overload or misconfigurations. Techniques for anomaly traffic classification are often used to characterize suspicious network traffic, thus supporting anomaly detection schemes in network resilience strategies. In this paper, we extend the PReSET toolset to allow the investigation, comparison and analysis of algorithms for anomaly traffic classification based on machine learning. PReSET was designed to allow the simulation-based evaluation of resilience strategies, thus enabling the comparison of optimal configurations and policies for combating different types of attacks (e.g., DDoS attacks, worms) and other anomalies. In such resilience strategies, policies written in the Ponder2 language can be used to activate/reconfigure traffic classification modules and other mechanisms (e.g., traffic shaping), depending on monitored results in the simulation environment. Our results show that PReSET can be a valuable tool for network operators to evaluate anomaly traffic classification techniques in terms of standard performance metrics.

U2 - 10.1109/ISCC.2015.7405566

DO - 10.1109/ISCC.2015.7405566

M3 - Conference contribution/Paper

SN - 9781467371957

SP - 514

EP - 519

BT - Proceedings of 20th IEEE Symposium on Computers and Communications, ISCC2015

PB - IEEE

ER -