Leveraging Siamese networks for one-shot intrusion detection model

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. 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 based only on one example is evaluated using . The results confirm the adapt- ability of the model in classifying unseen attacks and the trade-off between performance and the need for distinctive class representations.