Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS

• URL: http://arxiv.org/abs/2609.08623v1
• Date: Tue, 08 Sep 2026 11:58:12 GMT
• Title: Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS
• Abstract summary: We propose DriftXpert, a novel NIDS for drift-adaptive detection.<n>In Phase 1, we introduce an unsupervised anomaly metric based on latent manifold deviation.<n>In Phase 2, to mitigate catastrophic forgetting under non-stationary distributions, we design a representation consistency alignment strategy.
• Score: 1.9678915302433768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network 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. Specifically, we propose a decoupled two-stage offline adaptive framework. In Phase 1, we introduce an unsupervised anomaly metric based on latent manifold deviation. By performing outlier analysis within the latent space, the framework achieves high-sensitivity detection of network traffic concept drift. In Phase 2, to mitigate catastrophic forgetting under non-stationary distributions, we design a representation consistency alignment strategy. This strategy constrains the feature mapping between the legacy model and the drifted distribution, ensuring the model captures emerging attack characteristics while retaining discriminative power over known patterns. Furthermore, we incorporate cross-epoch neuron weight aggregation and selective freezing mechanisms to enable fine-grained knowledge transfer in the parameter space, effectively balancing model plasticity and stability. Extensive experiments on public datasets demonstrate that DriftXpert effectively adapts to drifted data without catastrophic forgetting. Furthermore, real-world evaluations on enterprise network further confirm its robustness and practical applicability, contributing to improved security protection for millions of users.

Related papers

• A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing [11.700555168494917]
  Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve.<n>Existing adaptive frameworks lack principled mechanisms for detecting when updates are needed.<n>We present an adaptive Digital Twin framework that integrates a Fisher score--based multivariate drift detector, Low-Rank Adaptation (LoRA) for parameter-efficient continual learning, and a Mann--Whitney $U$ test for online statistical validation.
  arXiv  Detail & Related papers  (2026-07-20T17:07:41Z)
• FIRCE: A Framework for Intrusion Response and Conformal Evaluation [3.3271517946886924]
  FIRCE is a framework for Intrusion Response and Conformal Evaluation.<n>It supports conformal evaluation-based uncertainty quantification and drift detection.<n>We demonstrate FIRCE's ability to detect distributional shifts and trigger model retraining.
  arXiv  Detail & Related papers  (2026-05-03T16:48:03Z)
• Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection via Latent Space Representation Learning and Alignment [2.5782420501870296]
  This paper proposes a scalable framework for adaptive IoT threat detection.<n>An alignment model maps incoming traffic to the learned historical latent space prior to classification.<n>To capture inter-instance relationships among attack samples, the low-dimensional latent representations are transformed into a graph-structured format.
  arXiv  Detail & Related papers  (2025-12-27T06:13:19Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Advancing Reliable Test-Time Adaptation of Vision-Language Models under Visual Variations [67.35596444651037]
  Vision-language models (VLMs) exhibit remarkable zero-shot capabilities but struggle with distribution shifts in downstream tasks when labeled data is unavailable.<n>We propose a Reliable Test-time Adaptation (ReTA) method that enhances reliability from two perspectives.
  arXiv  Detail & Related papers  (2025-07-13T05:37:33Z)
• Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection [14.728689487990836]
  generative active adaptation framework minimizes labeling effort while enhancing model robustness.<n>We evaluate our end-to-end framework NetGuard on both simulated IDS data and a real-world ISP dataset.
  arXiv  Detail & Related papers  (2025-03-04T21:49:42Z)
• Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection [6.3399691183255165]
  Intrusion Detection Systems (IDS) are crucial for safeguarding digital infrastructure.<n>In this paper, we propose SSF (Strategic Selection and Forgetting), a novel continual learning method for IDS.<n>Our approach features a strategic sample selection algorithm to select representative new samples and a strategic forgetting mechanism to drop outdated samples.
  arXiv  Detail & Related papers  (2024-12-20T09:22:07Z)
• Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [60.54852710216738]
  We introduce a novel digital twin-assisted optimization framework, called D-REC, to ensure reliable caching in nextG wireless networks. By incorporating reliability modules into a constrained decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints.
  arXiv  Detail & Related papers  (2024-06-29T02:40:28Z)
• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.