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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 \u201cOne-Shot Learning\u201d, 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\u201999. 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.<\/jats:p>","DOI":"10.1007\/s10844-022-00747-z","type":"journal-article","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T17:12:58Z","timestamp":1667668378000},"page":"407-436","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Leveraging siamese networks for one-shot intrusion detection model"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5195-8193","authenticated-orcid":false,"given":"Hanan","family":"Hindy","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9150-6805","authenticated-orcid":false,"given":"Christos","family":"Tachtatzis","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6206-2229","authenticated-orcid":false,"given":"Robert","family":"Atkinson","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9677-1445","authenticated-orcid":false,"given":"David","family":"Brosset","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2994-7826","authenticated-orcid":false,"given":"Miroslav","family":"Bures","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9093-5245","authenticated-orcid":false,"given":"Ivan","family":"Andonovic","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5132-4572","authenticated-orcid":false,"given":"Craig","family":"Michie","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1849-5788","authenticated-orcid":false,"given":"Xavier","family":"Bellekens","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,5]]},"reference":[{"issue":"4","key":"747_CR1","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","volume":"3","author":"H Altae-Tran","year":"2017","unstructured":"Altae-Tran, H., Ramsundar, B., Pappu, A.S., & Pande, V. 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