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.

Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.

We also establish new convergence complexities to achieve an approximate KKT solution when the objective can be smooth/nonsmooth, deterministic/stochastic and convex/nonconvex with complexity that is on a par with gradient descent for unconstrained optimization problems in respective cases. To the best of our knowledge, this is the first study of the first-order methods with complexity guarantee for nonconvex sparse-constrained problems.