Few-Shot Network Intrusion Detection Using Online Triplet Mining
Network intrusion detection systems play a vital role in protecting networks by detecting malicious network traffic which can then be investigated by a cybersecurity operations centre. State-of-the-art approaches utilise supervised machine learning methods to train a classification model to recognise known cyberattacks; however, these models require a large labelled dataset to train and show poor performance when trained on smaller datasets. In an attempt to address this shortcoming, anomaly detection models learn the distribution of benign traffic and flag non-conforming traffic as malicious. While these methods do not require malicious examples to train, they suffer from high false-posi...
| Authors | Jack Wilkie, Hanan Hindy, Christos Tachtatzis et al. |
|---|---|
| Primary category | cs.CR |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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