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OTM Confederated International Conferences "On the Move to Meaningful Internet Systems"

OTM 2019: On the Move to Meaningful Internet Systems: OTM 2019 Workshops pp 229–238Cite as

DeepNet: A Deep Learning Architecture for Network-Based Anomaly Detection

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11878))

Abstract

Anomaly detection has been one of the most interesting research areas in the field of cybersecurity. Supervised anomaly detection systems have not been practical and effective enough in real-world scenarios. As a result, different unsupervised anomaly detection pipelines have gained more attention due to their effectiveness. Autoencoders are one of the most powerful unsupervised approaches which can be used to analyze complex and large-scale datasets. This study proposes a method called DeepNet, which investigates the potential of adopting an unsupervised deep learning approach by proposing an autoencoder architecture to detect network intrusion. An autoencoder approach is implemented on network-based data while taking different architectures into account. We provide a comprehensive comparison of the effectiveness of different schemes. Due to the unique methodology of autoencoders, specific methods have been suggested to evaluate the performance of proposed models. The results of this study can be used as a foundation to build a robust anomaly detection system with an unsupervised approach.

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Author information

Authors and Affiliations

  1. Information Systems Department, University of Maryland, Baltimore County, MD, USA

    Javad Zabihi & Vandana Janeja

Authors
  1. Javad Zabihi
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  2. Vandana Janeja
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Corresponding author

Correspondence to Vandana Janeja .

Editor information

Editors and Affiliations

  1. Trinity College Dublin, Dublin, Ireland

    Christophe Debruyne

  2. University of Lorraine, Nancy, France

    Hervé Panetto

  3. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Wided Guédria

  4. Maastricht University, Maastricht, The Netherlands

    Peter Bollen

  5. Babes-Bolyai University, Cluj-Napoca, Romania

    Ioana Ciuciu

  6. University of Maryland, Baltimore County (UMBC), Baltimore, MD, USA

    George Karabatis

  7. Graz University of Technology, Graz, Austria

    Robert Meersman

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Zabihi, J., Janeja, V. (2020). DeepNet: A Deep Learning Architecture for Network-Based Anomaly Detection. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2019 Workshops. OTM 2019. Lecture Notes in Computer Science(), vol 11878. Springer, Cham. https://doi.org/10.1007/978-3-030-40907-4_25

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  • DOI: https://doi.org/10.1007/978-3-030-40907-4_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40906-7

  • Online ISBN: 978-3-030-40907-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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