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International Conference on e-Democracy

e-Democracy 2013: E-Democracy, Security, Privacy and Trust in a Digital World pp 11–23Cite as

A Hybrid Network Anomaly and Intrusion Detection Approach Based on Evolving Spiking Neural Network Classification

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 441))

Abstract

The evolution of network services is closely connected to the understanding and modeling of their corresponding traffic. The obtained conclusions are related to a wide range of applications, like the design of the transfer lines’ capacity, the scalar taxing of customers, the security violations and the spotting of errors and anomalies. Intrusion Detection Systems (IDS) monitor and analyze the events in traffic, to locate indications for potential intrusion and integrity violation attacks, resulting in the violation of trust and availability of information resources. They act in a complimentary mode with the existing security infrastructure, aiming in the early warning of the administrator, offering him details that will let him reach proper decisions and correction actions. This paper proposes a network-based online system, which uses minimum computational power to analyze only the basic characteristics of network flow, so as to spot the existence and the type of a potential network anomaly. It is a Hybrid Machine Learning Anomaly Detection System (HMLADS), which employs classification performed by Evolving Spiking Neural Networks (eSNN), in order to properly label a Potential Anomaly (PAN) in the net. On the other hand it uses a Multi-Layer Feed Forward (MLFF) ANN to classify the exact type of the intrusion.

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Author information

Authors and Affiliations

  1. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 193 Pandazidou st., 68200, N. Orestiada, Greece

    Konstantinos Demertzis & Lazaros Iliadis

Authors
  1. Konstantinos Demertzis
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  2. Lazaros Iliadis
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Corresponding author

Correspondence to Konstantinos Demertzis .

Editor information

Editors and Affiliations

  1. Agricultural University of Athens, Athens, Greece

    Alexander B. Sideridis

  2. Hellenic Data Protection Authority, Athens, Greece

    Zoe Kardasiadou

  3. Agricultural University of Athens, Athens, Greece

    Constantine P. Yialouris

  4. Hellenic Data Protection Authority, Athens, Greece

    Vasilios Zorkadis

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© 2014 Springer International Publishing Switzerland

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Demertzis, K., Iliadis, L. (2014). A Hybrid Network Anomaly and Intrusion Detection Approach Based on Evolving Spiking Neural Network Classification. In: Sideridis, A., Kardasiadou, Z., Yialouris, C., Zorkadis, V. (eds) E-Democracy, Security, Privacy and Trust in a Digital World. e-Democracy 2013. Communications in Computer and Information Science, vol 441. Springer, Cham. https://doi.org/10.1007/978-3-319-11710-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-11710-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11709-6

  • Online ISBN: 978-3-319-11710-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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