Comparing the predictive performance and computational cost of flow-based intrusion detection experiments requires both quantities to be available for each run. GreenNIDS connects benchmark execution on public NetFlow datasets to a reusable analysis dataset. Model implementations write a common set of outputs, which are ingested into a run-level table with companion tables for model configuration, feature importance, and training history. The run-level table combines predictive metrics with timing, throughput, memory use, and energy estimates based on thermal design power. A CPU-only demonstration comprises 216 runs across four dataset families, three versions, and binary and multiclass tasks. Ingestion produces a 216 x 96 analysis table with no duplicate run identifiers and complete Matthews correlation coefficient (MCC) and F1 coverage. Multiclass ROC-AUC is missing for 36 PyTorch runs, making metric availability an explicit part of subsequent comparisons. The stored records support grouped performance and training-time plots, as well as resource screening after execution. Combining task-specific MCC thresholds with limits on training time, peak RAM, and estimated training energy retains 25.0% of the full run collection. These results establish compatibility within the evaluated collection and support comparisons after execution, subject to the recorded preprocessing, execution profile, and approximate energy accounting.
Network intrusion detection identifies malicious or abnormal network activity to help protect computers, connected devices, and critical infrastructure.
RENCI, University of North Carolina at Chapel Hill Chapel Hill, NC, USA · Dept. of Computer and Information Science Fordham University New York, NY, USA