GreenNIDS: A Benchmark Workflow and Analysis Dataset for NetFlow Intrusion Detection Experiments
Comparing the predictive performance and computational cost of flow-based intrusion detection experiments requires both quantities to be available for each run.
ProofPaper ↗
Key points
- GreenNIDS connects benchmark execution on public NetFlow datasets to a reusable analysis dataset.
- 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.
- Multiclass ROC-AUC is missing for 36 PyTorch runs, making metric availability an explicit part of subsequent comparisons.
Sources (1)
- [1]GreenNIDS: A Benchmark Workflow and Analysis Dataset for NetFlow Intrusion Detection ExperimentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 04:11 PM
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.
Extractive summary: sentences quoted from the sources.
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