Graph Neural Networks Based Anomalous RSSI Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917280094355456 |
|---|---|
| author | Bertalanič, Blaž Vnučec, Matej Fortuna, Carolina |
| author_facet | Bertalanič, Blaž Vnučec, Matej Fortuna, Carolina |
| contents | In today's world, modern infrastructures are being equipped with information and communication technologies to create large IoT networks.
It is essential to monitor these networks to ensure smooth operations by detecting and correcting link failures or abnormal network behaviour proactively, which can otherwise cause interruptions in business operations.
This paper presents a novel method for detecting anomalies in wireless links using graph neural networks. The proposed approach involves converting time series data into graphs and training a new graph neural network architecture based on graph attention networks that successfully detects anomalies at the level of individual measurements of the time series data. The model provides competitive results compared to the state of the art while being computationally more efficient with ~171 times fewer trainable parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15847 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks Based Anomalous RSSI Detection Bertalanič, Blaž Vnučec, Matej Fortuna, Carolina Networking and Internet Architecture Machine Learning Signal Processing In today's world, modern infrastructures are being equipped with information and communication technologies to create large IoT networks. It is essential to monitor these networks to ensure smooth operations by detecting and correcting link failures or abnormal network behaviour proactively, which can otherwise cause interruptions in business operations. This paper presents a novel method for detecting anomalies in wireless links using graph neural networks. The proposed approach involves converting time series data into graphs and training a new graph neural network architecture based on graph attention networks that successfully detects anomalies at the level of individual measurements of the time series data. The model provides competitive results compared to the state of the art while being computationally more efficient with ~171 times fewer trainable parameters. |
| title | Graph Neural Networks Based Anomalous RSSI Detection |
| topic | Networking and Internet Architecture Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2505.15847 |