A Representation Learning Approach to Feature Drift Detection in Wireless Networks

• URL: http://arxiv.org/abs/2505.10325v1
• Date: Thu, 15 May 2025 14:08:00 GMT
• Title: A Representation Learning Approach to Feature Drift Detection in Wireless Networks
• Authors: Athanasios Tziouvaras, Blaz Bertalanic, George Floros, Kostas Kolomvatsos, Panagiotis Sarigiannidis, Carolina Fortuna,
• Abstract summary: We propose ALERT; a method that can detect feature distribution changes and trigger model re-training.<n> ALERT includes three components: representation learning, statistical testing and utility assessment.<n>We show the superiority of the proposed method against ten standard drift detection methods available in the literature.
• Score: 4.974285692877258
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI is foreseen to be a centerpiece in next generation wireless networks enabling enabling ubiquitous communication as well as new services. However, in real deployment, feature distribution changes may degrade the performance of AI models and lead to undesired behaviors. To counter for undetected model degradation, we propose ALERT; a method that can detect feature distribution changes and trigger model re-training that works well on two wireless network use cases: wireless fingerprinting and link anomaly detection. ALERT includes three components: representation learning, statistical testing and utility assessment. We rely on MLP for designing the representation learning component, on Kolmogorov-Smirnov and Population Stability Index tests for designing the statistical testing and a new function for utility assessment. We show the superiority of the proposed method against ten standard drift detection methods available in the literature on two wireless network use cases.

Related papers

• A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks [9.579593025301936]
  Existing methods for anomaly detection often face challenges such as the absence of sample labels, few anomaly samples, and an imbalanced sample distribution.<n>To address these issues, a correlation detection model (MTAD-RD) is proposed.<n>MTAD-RD can integrate the intermodal correlation features and spatial internode neighbors while extracting global information from time series data.<n>Its serialized inference characteristic nodes can remarkably reduce inference overhead.
  arXiv  Detail & Related papers  (2025-05-31T06:50:05Z)
• Decision Feedback In-Context Learning for Wireless Symbol Detection [17.27423651223751]
  Transformer-based wireless receivers, where prompts consist of the pilot data in the form of transmitted and received signal pairs, have shown high detection accuracy when pilot data are abundant.<n>We propose DEcision Feedback IN-ContExt Detection (DEFINED) as a new wireless receiver design, which bypasses channel estimation and directly performs symbol detection using the limited pilot data.
  arXiv  Detail & Related papers  (2025-03-20T17:57:01Z)
• Feature Selection for Network Intrusion Detection [3.7414804164475983]
  We present a novel information-theoretic method that facilitates the exclusion of non-informative features when detecting network intrusions. The proposed method is based on function approximation using a neural network, which enables a version of our approach that incorporates a recurrent layer.
  arXiv  Detail & Related papers  (2024-11-18T14:25:55Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN [5.381374943525773]
  Defect detection is crucial for ensuring the trustworthiness of railway systems. Current approaches rely on single deep-learning models, like CNNs. We propose a reusable AI-enabled defect detection approach.
  arXiv  Detail & Related papers  (2023-11-24T19:45:55Z)
• Cross-Inferential Networks for Source-free Unsupervised Domain Adaptation [17.718392065388503]
  We propose to explore a new method called cross-inferential networks (CIN) Our main idea is that, when we adapt the network model to predict the sample labels from encoded features, we use these prediction results to construct new training samples with derived labels. Our experimental results on benchmark datasets demonstrate that our proposed CIN approach can significantly improve the performance of source-free UDA.
  arXiv  Detail & Related papers  (2023-06-29T14:04:24Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection [64.1680666036655]
  We revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view. We have evaluated the mathematical model using two different datasets.
  arXiv  Detail & Related papers  (2023-02-02T13:41:18Z)
• Modeling Uncertain Feature Representation for Domain Generalization [49.129544670700525]
  We show that our method consistently improves the network generalization ability on multiple vision tasks. Our methods are simple yet effective and can be readily integrated into networks without additional trainable parameters or loss constraints.
  arXiv  Detail & Related papers  (2023-01-16T14:25:02Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)

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.