Autoencoder Residual Augmented XGBoost with HDBSCAN Clustering for Zero-Shot Network Intrusion Detection on CIC-IDS-2017
A Network Intrusion Detection System (NIDS) must be able to detect zero-shot attack classes not present in their training set. Traditional supervised models cannot do this task, and purely unsupervised methods generate too many false positives. This paper describes a hybrid approach consisting of a deep autoencoder tra...