Improving Intrusion Detection Model Prediction by Threshold Adaptation
Network traffic exhibits a high level of variability over short periods of time.This variability impacts negatively on the accuracy of anomaly-based network intrusion detection Flatware Sets systems (IDS) that are built using predictive models in a batch learning setup.This work investigates how adapting the discriminating threshold of model predic