Jamming Detection in Cell-Free MIMO with Dynamic Graphs

• URL: http://arxiv.org/abs/2601.06075v1
• Date: Mon, 29 Dec 2025 15:06:14 GMT
• Title: Jamming Detection in Cell-Free MIMO with Dynamic Graphs
• Authors: Ali Hossary, Laura Crosara, Stefano Tomasin,
• Abstract summary: Jamming attacks pose a critical threat to wireless networks.<n>This paper proposes a novel jamming detection framework leveraging dynamic graphs and graph convolutional neural networks.<n>A GCN-Transformer-based model, trained with supervised learning, learns graph embeddings to identify malicious interference.
• Score: 13.539599439234754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Jamming attacks pose a critical threat to wireless networks, particularly in cell-free massive MIMO systems, where distributed access points and user equipment (UE) create complex, time-varying topologies. This paper proposes a novel jamming detection framework leveraging dynamic graphs and graph convolutional neural networks (GCN) to address this challenge. By modeling the network as a dynamic graph, we capture evolving communication links and detect jamming attacks as anomalies in the graph evolution. A GCN-Transformer-based model, trained with supervised learning, learns graph embeddings to identify malicious interference. Performance evaluation in simulated scenarios with moving UEs, varying jamming conditions and channel fadings, demonstrates the method's effectiveness, which is assessed through accuracy and F1 score metrics, achieving promising results for effective jamming detection.

Related papers

• Graph Signal Generative Diffusion Models [74.75869068073577]
  We introduce U-shaped encoder-decoder graph neural networks (U-GNNs) for graph signal generation using denoising diffusion processes.<n>The architecture learns node features at different resolutions with skip connections between the encoder and decoder paths.<n>We demonstrate the effectiveness of the diffusion model in probabilistic forecasting of stock prices.
  arXiv  Detail & Related papers  (2025-09-21T21:57:27Z)
• Self-Supervised Learning of Graph Representations for Network Intrusion Detection [6.453778601809096]
  GraphIDS is a self-supervised intrusion detection model that unifies representation learning and anomaly detection.<n>An inductive graph neural network embeds each flow with its local topological context to capture typical network behavior.<n>A Transformer-based encoder-decoder reconstructs these embeddings, implicitly learning global co-occurrence patterns via self-attention.<n>During inference, flows with unusually high reconstruction errors are flagged as potential intrusions.
  arXiv  Detail & Related papers  (2025-09-20T11:02:50Z)
• Power Grid Control with Graph-Based Distributed Reinforcement Learning [60.49805771047161]
  This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management.<n>A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation.<n>Experiments on the Grid2Op simulation environment show the effectiveness of the approach.
  arXiv  Detail & Related papers  (2025-09-02T22:17:25Z)
• Graph Neural Networks Based Anomalous RSSI Detection [0.196629787330046]
  This paper presents a novel method for detecting anomalies in wireless links using graph neural networks.<n>The proposed approach involves converting time series data into graphs and training a new graph neural network architecture.
  arXiv  Detail & Related papers  (2025-05-19T09:16:32Z)
• A Deep Learning Framework for Evaluating Dynamic Network Generative Models and Anomaly Detection [0.0]
  This paper introduces DGSP-GCN (Dynamic Graph Similarity Prediction based on Graph Convolutional Network)<n>It integrates graph convolutional networks with dynamic graph signal processing techniques to provide a unified solution for evaluating generative models and detecting anomalies in dynamic networks.<n>It was tested on five real-world datasets: WikiMath, Chickenpox, PedalMe, MontevideoBus, and MetraLa.
  arXiv  Detail & Related papers  (2024-06-15T09:19:09Z)
• Multitask Active Learning for Graph Anomaly Detection [48.690169078479116]
  We propose a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE. By coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies. Empirical studies on four datasets demonstrate that MITIGATE significantly outperforms the state-of-the-art methods for anomaly detection.
  arXiv  Detail & Related papers  (2024-01-24T03:43:45Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids [0.3093890460224435]
  This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. The sensitivity of the model to intensity and location of the attacks and model's detection delay versus detection accuracy have been evaluated.
  arXiv  Detail & Related papers  (2022-12-07T00:56:02Z)
• Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks [7.718169412279379]
  False data injection attacks (FDIA) are more frequently encountered in power systems. This paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems.
  arXiv  Detail & Related papers  (2021-04-24T00:33:45Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs [54.13919050090926]
  We propose an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection.
  arXiv  Detail & Related papers  (2020-05-15T09:17:08Z)
• Adversarial Attack on Community Detection by Hiding Individuals [68.76889102470203]
  We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model.
  arXiv  Detail & Related papers  (2020-01-22T09:50:04Z)

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.