Labelled network capture generation for anomaly detection

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
203 Downloads (Pure)


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 languageEnglish
Title of host publicationFoundations and practice of security
Subtitle of host publication12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers
EditorsAbdelmalek Benzekri, Michel Barbeau, Guang Gong, Romain Laborde, Joaquin Garcia-Alfaro
Place of PublicationCham
Number of pages16
ISBN (Electronic)9783030453718
ISBN (Print)9783030453701
Publication statusPublished - 17 Apr 2020
Event12th International Symposium on Foundations and Practice of Security - Crowne Plaza, Toulouse, France
Duration: 5 Nov 20197 Nov 2019
Conference number: 12th

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLNCS Sublibrary: SL4 – Security and Cryptology


Conference12th International Symposium on Foundations and Practice of Security
Abbreviated titleFPS 2019
Internet address


  • Network traffic generation
  • Data analysis
  • Intrusion detection systems
  • Cyber security
  • Network security


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