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International Conference on Intelligent Systems Design and Applications

ISDA 2021: Intelligent Systems Design and Applications pp 548–558Cite as

Implementation-Oriented Feature Selection in UNSW-NB15 Intrusion Detection Dataset

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 418)

Abstract

With a daily global average of 30,000 websites breached in 2021, security challenges grow in difficulty, complexity, and importance. Since it’s publication, UNSW-NB15 dataset was used in many machine-learning, and statistics based intrusion detection solutions. It provides over 2.5 million instances of benign and malicious network flow captures.

In this paper, we present an implementation-oriented feature selection that reduces the number of features while maintaining high accuracy. The proposed reduction resulted in a dataset with 5 features that are focused on making machine learning models more implementable, practical, and efficient. Testing showed that the reduced dataset maintained an accuracy of 99% with a testing time reduction of up to 84%.

Keywords

  • Intrusion detection
  • Dataset
  • IoT
  • Malware

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References

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Authors and Affiliations

  1. School of Information Technology Administration and Security, Seneca College of Applied Arts and Technology, Toronto, ON, Canada

    Mohammed M. Alani

Authors
  1. Mohammed M. Alani
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Corresponding author

Correspondence to Mohammed M. Alani .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Dr. Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Dr. Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Dr. Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Dr. Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Dr. Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Dr. Weiping Ding

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Cite this paper

Alani, M.M. (2022). Implementation-Oriented Feature Selection in UNSW-NB15 Intrusion Detection Dataset. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_51

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