Labelled network capture generation for anomaly detection

Maël Nogues; David  Brosset; Hanan Hindy; Xavier Bellekens; Yvon Kermarrec

doi:10.1007/978-3-030-45371-8_7

Labelled network capture generation for anomaly detection

Maël Nogues^*, David Brosset, Hanan Hindy, Xavier Bellekens, Yvon Kermarrec

^*Corresponding author for this work

SDI

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

In the race to simplify man-machine interactions and maintenance processes, hardware is increasingly interconnected. With more connected devices than ever, in our homes and workplaces, the attack surface is increasing tremendously. To detect this growing flow of cyber-attacks, machine learning based intrusion detection systems are being deployed at an unprecedented pace. In turn, these require a constant feed of data to learn and differentiate normal traffic from abnormal traffic. Unfortunately, there is a lack of learning datasets available. In this paper, we present a software platform generating fully labelled datasets for data analysis and anomaly detection.

Original language	English
Title of host publication	Foundations and practice of security
Subtitle of host publication	12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers
Editors	Abdelmalek Benzekri, Michel Barbeau, Guang Gong, Romain Laborde, Joaquin Garcia-Alfaro
Place of Publication	Cham
Publisher	Springer
Pages	98-113
Number of pages	16
ISBN (Electronic)	9783030453718
ISBN (Print)	9783030453701
DOIs	https://doi.org/10.1007/978-3-030-45371-8_7
Publication status	Published - 17 Apr 2020
Event	12th International Symposium on Foundations and Practice of Security - Crowne Plaza, Toulouse, France Duration: 5 Nov 2019 → 7 Nov 2019 Conference number: 12th https://fps2019.sciencesconf.org/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	12056
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	LNCS Sublibrary: SL4 – Security and Cryptology
Publisher	Springer

Conference

Conference	12th International Symposium on Foundations and Practice of Security
Abbreviated title	FPS 2019
Country	France
City	Toulouse
Period	5/11/19 → 7/11/19
Internet address	https://fps2019.sciencesconf.org/

Access to Document

10.1007/978-3-030-45371-8_7

Hindy_LabelledNetworkCaptureGeneration_Accepted_2020Accepted author manuscript, 2.91 MB

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Nogues, M., Brosset, D., Hindy, H., Bellekens, X., & Kermarrec, Y. (2020). Labelled network capture generation for anomaly detection. In A. Benzekri, M. Barbeau, G. Gong, R. Laborde, & J. Garcia-Alfaro (Eds.), Foundations and practice of security: 12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers (pp. 98-113). (Lecture Notes in Computer Science; Vol. 12056), (LNCS Sublibrary: SL4 – Security and Cryptology). Springer. https://doi.org/10.1007/978-3-030-45371-8_7

Nogues, Maël ; Brosset, David ; Hindy, Hanan ; Bellekens, Xavier ; Kermarrec, Yvon. / Labelled network capture generation for anomaly detection. Foundations and practice of security: 12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers. editor / Abdelmalek Benzekri ; Michel Barbeau ; Guang Gong ; Romain Laborde ; Joaquin Garcia-Alfaro. Cham : Springer, 2020. pp. 98-113 (Lecture Notes in Computer Science). (LNCS Sublibrary: SL4 – Security and Cryptology).

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title = "Labelled network capture generation for anomaly detection",

abstract = "In the race to simplify man-machine interactions and maintenance processes, hardware is increasingly interconnected. With more connected devices than ever, in our homes and workplaces, the attack surface is increasing tremendously. To detect this growing flow of cyber-attacks, machine learning based intrusion detection systems are being deployed at an unprecedented pace. In turn, these require a constant feed of data to learn and differentiate normal traffic from abnormal traffic. Unfortunately, there is a lack of learning datasets available. In this paper, we present a software platform generating fully labelled datasets for data analysis and anomaly detection.",

author = "Ma{\"e}l Nogues and David Brosset and Hanan Hindy and Xavier Bellekens and Yvon Kermarrec",

year = "2020",

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language = "English",

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editor = "Abdelmalek Benzekri and Michel Barbeau and Guang Gong and Romain Laborde and Joaquin Garcia-Alfaro",

booktitle = "Foundations and practice of security",

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Nogues, M, Brosset, D, Hindy, H, Bellekens, X & Kermarrec, Y 2020, Labelled network capture generation for anomaly detection. in A Benzekri, M Barbeau, G Gong, R Laborde & J Garcia-Alfaro (eds), Foundations and practice of security: 12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers. Lecture Notes in Computer Science, vol. 12056, LNCS Sublibrary: SL4 – Security and Cryptology, Springer, Cham, pp. 98-113, 12th International Symposium on Foundations and Practice of Security, Toulouse, France, 5/11/19. https://doi.org/10.1007/978-3-030-45371-8_7

Labelled network capture generation for anomaly detection. / Nogues, Maël; Brosset, David ; Hindy, Hanan; Bellekens, Xavier; Kermarrec, Yvon.

Foundations and practice of security: 12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers. ed. / Abdelmalek Benzekri; Michel Barbeau; Guang Gong; Romain Laborde; Joaquin Garcia-Alfaro. Cham : Springer, 2020. p. 98-113 (Lecture Notes in Computer Science; Vol. 12056), (LNCS Sublibrary: SL4 – Security and Cryptology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Labelled network capture generation for anomaly detection

AU - Nogues, Maël

AU - Brosset, David

AU - Hindy, Hanan

AU - Bellekens, Xavier

AU - Kermarrec, Yvon

N1 - Conference code: 12th

PY - 2020/4/17

Y1 - 2020/4/17

N2 - In the race to simplify man-machine interactions and maintenance processes, hardware is increasingly interconnected. With more connected devices than ever, in our homes and workplaces, the attack surface is increasing tremendously. To detect this growing flow of cyber-attacks, machine learning based intrusion detection systems are being deployed at an unprecedented pace. In turn, these require a constant feed of data to learn and differentiate normal traffic from abnormal traffic. Unfortunately, there is a lack of learning datasets available. In this paper, we present a software platform generating fully labelled datasets for data analysis and anomaly detection.

AB - In the race to simplify man-machine interactions and maintenance processes, hardware is increasingly interconnected. With more connected devices than ever, in our homes and workplaces, the attack surface is increasing tremendously. To detect this growing flow of cyber-attacks, machine learning based intrusion detection systems are being deployed at an unprecedented pace. In turn, these require a constant feed of data to learn and differentiate normal traffic from abnormal traffic. Unfortunately, there is a lack of learning datasets available. In this paper, we present a software platform generating fully labelled datasets for data analysis and anomaly detection.

U2 - 10.1007/978-3-030-45371-8_7

DO - 10.1007/978-3-030-45371-8_7

M3 - Conference contribution

SN - 9783030453701

T3 - Lecture Notes in Computer Science

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EP - 113

BT - Foundations and practice of security

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A2 - Gong, Guang

A2 - Laborde, Romain

A2 - Garcia-Alfaro, Joaquin

PB - Springer

CY - Cham

T2 - 12th International Symposium on Foundations and Practice of Security

Y2 - 5 November 2019 through 7 November 2019

ER -

Nogues M, Brosset D, Hindy H, Bellekens X, Kermarrec Y. Labelled network capture generation for anomaly detection. In Benzekri A, Barbeau M, Gong G, Laborde R, Garcia-Alfaro J, editors, Foundations and practice of security: 12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers. Cham: Springer. 2020. p. 98-113. (Lecture Notes in Computer Science). (LNCS Sublibrary: SL4 – Security and Cryptology). https://doi.org/10.1007/978-3-030-45371-8_7