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Deep Learning-Based IDSs

  • Kwangjo Kim
  • Muhamad Erza Aminanto
  • Harry Chandra Tanuwidjaja
Chapter
Part of the SpringerBriefs on Cyber Security Systems and Networks book series (BRIEFSCSSN)

Abstract

This chapter reviews recent IDSs leveraging deep learning models as their methodology which were published during 2016 and 2017. The critical issues like problem domain, methodology, dataset, and experimental result of each publication will be discussed. These publications can be classified into three different categories according to deep learning classification in Chap.  4, namely, generative, discriminative, and hybrid. The generative model group consists of IDSs that use deep learning models for feature extraction only and use shallow methods for the classification task. The discriminative model group contains IDSs that use a single deep learning method for both feature extraction and classification task. The hybrid model group includes IDSs that use more than one deep learning method for generative and discriminative purposes. All IDSs are compared to overview the advancement of deep learning in IDS researches.

Keywords

Deep Learning Models Shallow Methods Short-Term Memory Recurrent Neural Networks Distributed Denial Of Service (DDoS) Collective Anomalies 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Kwangjo Kim
    • 1
  • Muhamad Erza Aminanto
    • 1
  • Harry Chandra Tanuwidjaja
    • 1
  1. 1.School of Computing (SoC)Korea Advanced Institute of Science and TechnologyDaejeonKorea (Republic of)

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