TAN-IDS: A Transfer-Aware Evaluation Framework for NetFlow-Based Intrusion Detection

08 Apr 20264 min read0 viewsJournal Feed

GIST

by Dung Ha Thanh Machine learning-based Intrusion Detection Systems (IDS) often report high detection accuracy under controlled, single-dataset evaluation, yet experience severe performance degradation when deployed in unseen network environments due to domain shift. To bridge this gap between laboratory benchmarking and real-world deployment, this paper presents TAN-IDS, a transfer-aware and deployment-oriented evaluation framework for NetFlow-based intrusion detection.

Rather than proposing a new detection model, TAN-IDS contributes a methodological evaluation framework that unifies heterogeneous traffic datasets under a compact 8-dimensional NetFlow feature interface. This constrained representation supports interoperable and deployment-realistic evaluation across datasets collected in different network settings, enabling performance degradation to be more reliably attributed to domain shift rather than feature-space incompatibilities.

Within this unified interface, TAN-IDS formalizes key deployment conditions as explicit evaluation scenarios, including in-dataset evaluation, direct cross-dataset transfer, mixed-domain training, and lightweight target-domain fine-tuning.

Clinical Editorial

Study aim and design context

The work focuses on addressing the gap between laboratory benchmarking and real-world deployment for machine learning–based intrusion detection systems (IDS).
Rather than introducing a new detection model, the authors present TAN-IDS, a transfer-aware, deployment-oriented evaluation framework that unifies heterogeneous NetFlow datasets via a compact 8-dimensional feature interface.
The framework is designed to attribute performance degradation to domain shift rather than feature-space incompatibilities, by harmonizing data representation across networks and settings.

Unified representation and evaluation scenarios

TAN-IDS formalizes deployment conditions as explicit evaluation scenarios within the unified NetFlow interface: in-dataset evaluation, direct cross-dataset transfer, mixed-domain training, and lightweight target-domain fine-tuning.
The 8-feature NetFlow interface enables interoperable, deployment-realistic evaluation across datasets collected in diverse network environments.
The approach emphasizes diagnosing robustness gaps attributed to domain shift rather than mere feature availability.

Empirical findings and model implications

Experiments use representative machine learning models and neural architectures, including a lightweight Transformer-based control model.
Results show that strong performance within a single dataset does not guarantee cross-dataset robustness.
Increasing model complexity by itself does not reliably mitigate domain shift.
Domain-aware training strategies yield meaningful gains: mixed-domain training improves generalization, and limited target-domain fine-tuning with about 5% labeled data substantially recovers recall for attack classes and improves F1-macro, achieving above 95% in several scenarios.

Operational relevance and limitations

TAN-IDS offers a reproducible, deployment-centric evaluation framework that exposes robustness limitations often missed by traditional benchmark-centric assessments.
The framework’s emphasis on explicit deployment scenarios and minimal target-domain labeling highlights practical strategies for improving cross-environment IDS performance.