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GreenNIDS: A Benchmark Workflow and Analysis Dataset for NetFlow Intrusion Detection Experiments

Lilia-Eliana Popescu-Apreutesei, Sabina-Cristiana Necula · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25366 · cited by 0 works (OpenAlex) · open-access PDF

Korean

흐름 기반 침입 탐지 실험의 예측 성과와 계산 비용을 비교하면 각 실행에 대해 양이 모두 사용할 수 있어야합니다. GreenNIDS는 공공 NetFlow 데이터 세트의 벤치마크 실행을 재사용 가능한 분석 데이터 세트에 연결합니다. 모델 구현은 흐름 기반 침입 감지 실험의 일반 세트를 작성합니다. 벤치마크 실행은 모델 구성, 기능 중요성 및 훈련 기록을위한 동반 테이블과 함께 실행 수준 테이블에 삽입됩니다. 벤치마크 실행 테이블은 타이밍, 통과량, 메모리 사용 및 열 설계 전력에 기반한 에너지 예측을 결합합니다. CPU-only 데모는 4 개 데이터 세트 가족, 3 개 버전 및 바이너리

Machine translation (Meta M2M100); the English original follows.

Abstract

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.

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Topics: Computer science, NetFlow, Data mining, Benchmark (surveying), Intrusion detection system, Workflow

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange