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INNS Conference on Big Data

INNS 2016: Advances in Big Data pp 39–49Cite as

Attribute Learning for Network Intrusion Detection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 529))

Abstract

Network intrusion detection is one of the most visible uses for Big Data analytics. One of the main problems in this application is the constant rise of new attacks. This scenario, characterized by the fact that not enough labeled examples are available for the new classes of attacks is hardly addressed by traditional machine learning approaches. New findings on the capabilities of Zero-Shot learning (ZSL) approach makes it an interesting solution for this problem because it has the ability to classify instances of unseen classes. ZSL has inherently two stages: the attribute learning and the inference stage. In this paper we propose a new algorithm for the attribute learning stage of ZSL. The idea is to learn new values for the attributes based on decision trees (DT). Our results show that based on the rules extracted from the DT a better distribution for the attribute values can be found. We also propose an experimental setup for the evaluation of ZSL on network intrusion detection (NID).

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Notes

  1. 1.

    KDD Cup is the annual Data Mining and Knowledge Discovery competition organized by ACM Special Interest Group on Knowledge Discovery and Data Mining.

  2. 2.

    Current research uses a small portion that represents the \(10\,\%\) of the original dataset containing 494, 021 instances.

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Author information

Authors and Affiliations

  1. CISUC - Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Jorge Luis Rivero Pérez & Bernardete Ribeiro

Authors
  1. Jorge Luis Rivero Pérez
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  2. Bernardete Ribeiro
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Corresponding author

Correspondence to Jorge Luis Rivero Pérez .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

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© 2017 Springer International Publishing AG

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Pérez, J.L.R., Ribeiro, B. (2017). Attribute Learning for Network Intrusion Detection. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-47898-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47897-5

  • Online ISBN: 978-3-319-47898-2

  • eBook Packages: EngineeringEngineering (R0)

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