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NSNAD: negative selection-based network anomaly detection approach with relevant feature subset

  • Naila Belhadj aissaEmail author
  • Mohamed Guerroumi
  • Abdelouahid Derhab
Original Article
  • 60 Downloads

Abstract

Intrusion detection systems are one of the security tools widely deployed in network architectures in order to monitor, detect and eventually respond to any suspicious activity in the network. However, the constantly growing complexity of networks and the virulence of new attacks require more adaptive approaches for optimal responses. In this work, we propose a semi-supervised approach for network anomaly detection inspired from the biological negative selection process. Based on a reduced dataset with a filter/ranking feature selection technique, our algorithm, namely negative selection for network anomaly detection (NSNAD), generates a set of detectors and uses them to classify events as anomaly. Otherwise, they are matched against an Artificial Human Leukocyte Antigen in order to be classified as normal. The accuracy and the computational time of NSNAD are tested under three intrusion detection datasets: NSL-KDD, Kyoto2006+ and UNSW-NB15. We compare the performance of NSNAD against a fully supervised algorithm (Naïve Bayes), an unsupervised clustering algorithm (K-means) and a semi-supervised algorithm (One-class SVM) with respect to multiple accuracy metrics. We also compare the time incurred by each algorithm in training and classification stages.

Keywords

Intrusion detection system (IDS) Anomaly detection Feature selection Artificial immune system (AIS) Negative selection NSL-KDD dataset Kyoto2006+ dataset UNSW-NB15 dataset 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electronic and Computer ScienceUniversity of Sciences and Technology Houari BoumedieneAlgiersAlgeria
  2. 2.Center of Excellence in Information Assurance (CoEIA)King Saud UniversityRiyadhKingdom of Saudi Arabia

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