Optimizing Weighted Ensemble ML IDS Using Parallelization and Batch Learning in Healthcare Networks

The increasing adoption of loT devices in healthcare necessitates the development of lightweight and fast intrusion de-tection systems (IDS). This paper proposes a weighted ensemble ML IDS with parallelized batch learning to optimize runtime while maintaining ML performance. The proposed method is compared to other solutions, evaluating ML performance and runtime across three different datasets. Experimental results demonstrate that although batch learning introduces feedback delay that slightly degrades ML performance, parallel computing significantly reduces runtime.