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.
- Two-phase framework detects concept drift via latent-space outlier analysis
- Representation alignment mitigates catastrophic forgetting under non-stationary traffic
- Cross-epoch weight aggregation and selective freezing balance plasticity and stability
- Validated on public datasets and a real enterprise network
- AI models
- DriftXpert
From the original article
1 min read at arxiv.orgNetwork intrusion detection systems (NIDS) are critical for cybersecurity, safeguarding services and data from potential attacks. However, existing AI-based NIDS often assume static data distributions and fail to handle concept drift, leading to degraded performance and increased false positives in dynamic network environments. To address this issue, we propose DriftXpert, a novel NIDS for drift-adaptive detection.
By Chao Zha · arxiv.org
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