Leveraging siamese networks for one-shot intrusion detection model

Hanan Hindy*, Christos Tachtatzis, Robert Atkinson, David Brosset, Miroslav Bures, Ivan Andonovic, Craig Michie, Xavier Bellekens*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems (IDS) has been the subject of significant research. Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to retrain the model for every unseen cyber-attack class. However, retraining the models in-situ renders the network susceptible to attacks owing to the time-window required to acquire a sufficient volume of data. Although anomaly detection systems provide a coarse-grained defence against unseen attacks, these approaches are significantly less accurate and suffer from high false-positive rates. Here, a complementary approach referred to as “One-Shot Learning”, whereby a limited number of examples of a new attack-class is used to identify a new attack-class (out of many) is detailed. The model grants a new cyber-attack classification opportunity for classes that were not seen during training without retraining. A Siamese Network is trained to differentiate between classes based on pairs similarities, rather than features, allowing to identify new and previously unseen attacks. The performance of a pre-trained model to classify new attack-classes based only on one example is evaluated using three mainstream IDS datasets; CICIDS2017, NSL-KDD, and KDD Cup’99. The results confirm the adaptability of the model in classifying unseen attacks and the trade-off between performance and the need for distinctive class representations.

Original languageEnglish
Number of pages30
JournalJournal of Intelligent Information Systems
Early online date5 Nov 2022
DOIs
Publication statusE-pub ahead of print - 5 Nov 2022

Keywords

  • Continuous lerning
  • Intrusion detection
  • Few-shot learning
  • Siamese network
  • CICIDS2017
  • NSL-KDD
  • Artificial Neural Network

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