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A First-Principles Evaluation of Graph-Based Network Intrusion Detection Systems
GIDS-Eval framework reveals evaluation gaps in graph-based network intrusion detection; two crafted edges fully evade three detector-dataset pairs.
Researchers introduce GIDS-Eval, a framework decomposing graph-based network intrusion detection systems into six interchangeable stages to enable controlled comparisons. Surveying nine GIDS and reimplementing five, they find two crafted edges achieve full evasion against three of eight detector-dataset pairs, snapshot windows alone cause a mean 38.3% relative swing in average precision, and none of 18 replayed detector-dataset pairs can alert as events arrive. Their encoder-free GIDS-Lite control ranks first by AP on two of four datasets at up to 575x lower runtime.
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Few-Shot Learning for Network Intrusion Detection: Methods, Datasets, and Performance
A PRISMA-style review of 21 few-shot learning studies for network intrusion detection finds meta-learning and CNNs dominant and evaluation inconsistently reported.
The systematic review screened 1,358 records from ACM Digital Library, IEEE Xplore, and Scopus covering 2022-2026 and retained 21 studies on few-shot learning for network intrusion detection. Meta-learning (8 studies) and convolutional neural networks (10) are the most common approaches, while CIC-IDS2017 and CSE-CIC-IDS2018 are the most frequently used datasets. Most evaluations use five or fewer samples per class, and missing parameters and source code limit reproducibility and direct comparison.
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Poster: Towards Selecting Threat Appropriate Industrial Intrusion Detection Systems
Poster proposes counter-threat-intelligence-based detector selection for industrial control systems, showing IDS performance varies strongly by attack scenario.
The poster proposes a counter-threat intelligence sharing mechanism to select appropriate intrusion detection systems for the current threat situation in industrial control system environments. Attack-level performance evaluations of various IDSs show detection performance varies depending on the attack scenario. The results emphasize the benefit of dynamically matching detectors to evolving ICS threats.
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Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection
RECAL improves provenance-based APT detection with relation-balanced masked graph learning and calibrated errors, reaching 99.99% F1 on DARPA E3 datasets.
RECAL is an unsupervised framework for provenance-based intrusion detection that uses relation-balanced masked graph learning to capture rare interaction patterns, addressing statistical heterogeneity where relation frequencies differ by roughly 140,000X in CADETS. It calibrates reconstruction errors against each relation's benign error distribution to produce comparable anomaly evidence and reduce false alarms. On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99%, 99.93%, and 99.99%, outperforming the best baseline on each dataset, and reduces mean false positive rate by approximately 105X, 4X, and 41X versus the lowest-FPR baseline.
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Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS
Researchers propose DriftXpert, a concept-drift-adaptive network intrusion detection system validated on enterprise networks, addressing degraded AI-based NIDS performance in dynamic traffic.
AI-based network intrusion detection systems assume static data distributions and degrade under concept drift, raising false positives in dynamic environments. DriftXpert uses a two-stage offline framework: an unsupervised latent-manifold anomaly metric to detect traffic drift, and representation consistency alignment with cross-epoch neuron weight aggregation and selective freezing to transfer knowledge without catastrophic forgetting. Experiments on public datasets and a real enterprise network show effective adaptation to drifted data while retaining known-attack detection.
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