  The use of supervised Machine Learning (ML) to enhance Intrusion Detection
Systems 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 re-train 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
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 attack-classes based only on one example is evaluated using three
datasets. Results confirm the adaptability of the model in classifying unseen
attacks and the trade-off between performance and the need for distinctive
class representation.
