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

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


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%.


  • Intrusion detection
  • Dataset
  • IoT
  • Malware

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Correspondence to Mohammed M. Alani .

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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.

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