A Deep Learning Approach for Network Intrusion Detection System

Authors

  • Ahmad Javaid University of Toledo image/svg+xml
  • Quamar Niyaz University of Toledo
  • Weiqing Sun University of Toledo
  • Mansoor Alam University of Toledo

DOI:

https://doi.org/10.4108/eai.3-12-2015.2262516

Keywords:

network security, nids, deep learning, sparse autoencoder, nsl-kdd

Abstract

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD - a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

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Published

24-05-2016

Issue

Vol. 3 No. 9 (2016): EAI Endorsed Transactions on Security and Safety

Section

Research article

License

Copyright (c) 2022 EAI Endorsed Transactions on Security and Safety

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License.

This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.

How to Cite

A Deep Learning Approach for Network Intrusion Detection System. (2016). EAI Endorsed Transactions on Security and Safety, 3(9), e2. https://doi.org/10.4108/eai.3-12-2015.2262516

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