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Feature Extraction and Malware Detection on Large HTTPS Data Using MapReduce

  • Přemysl ČechEmail author
  • Jan Kohout
  • Jakub Lokoč
  • Tomáš Komárek
  • Jakub Maroušek
  • Tomáš Pevný
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9939)

Abstract

Secure HTTP network traffic represents a challenging immense data source for machine learning tasks. The tasks usually try to learn and identify infected network nodes, given only limited traffic features available for secure HTTP data. In this paper, we investigate the performance of grid histograms that can be used to aggregate traffic features of network nodes considering just 5-min batches for snapshots. We compare the representation using linear and k-NN classifiers. We also demonstrate that all presented feature extraction and classification tasks can be implemented in a scalable way using the MapReduce approach.

Keywords

Hadoop MapReduce HTTPS data Intrusion detection Approximate similarity join 

Notes

Acknowledgments

This project was supported by the GAČR 15-08916S and GAUK 201515 grants.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Přemysl Čech
    • 1
    Email author
  • Jan Kohout
    • 2
  • Jakub Lokoč
    • 1
  • Tomáš Komárek
    • 2
  • Jakub Maroušek
    • 1
  • Tomáš Pevný
    • 2
  1. 1.SIRET Research Group, Faculty of Mathematics and Physics, Department of Software EngineeringCharles University in PraguePragueCzech Republic
  2. 2.FEE, Cognitive Research Center in PragueCzech Technical University in Prague, Cisco Systems, Inc.PragueCzech Republic

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