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Labelled Network Capture Generation for Anomaly Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12056)

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.

Keywords

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

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Chair of Naval Cyber Defence, École Navale - CC 600Brest Cedex 9France
  2. 2.Naval Academy Research Institute, École Navale - CC 600Brest Cedex 9France
  3. 3.Division of Cyber-SecurityAbertay UniversityDundeeUK
  4. 4.Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK
  5. 5.Institut Mines-Télécom Atlantique, Lab-STICC CNRS UMR 6285BrestFrance

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