Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection

Network intrusion detection system Continual learning with shallow methods Detailed illustration of configuration changes Datasets details Data preprocessing and feature selection Task formulation Task similarity via optimal transport dataset distance Training time comparison of the proposed ECBRS with the baselines Additional experiments with anomaly detection datasets Ablation studies Implementation, hardware details, and hyperparameter selection Occurrence of task dissimilarity between two different tasks is rare Limitations and broader impact A.1 Network intrusion detection system NID comprises two parts: the training module and the anomaly detection engine. The training can be periodic or triggered by an event like decay in intrusion detection accuracy. These features are fed to the anomaly detection engine to identify anomaly pattern(s). In our work, shallow methods are the non-neural network-based approaches. BWT is the influence that learning a task ' t ' has on the performance of BWT occurs when learning a task diminishes proficiency in prior tasks.