GAT-AD: Graph Attention Networks for contextual anomaly detection in network monitoring

H Latif-Martínez, J Suárez-Varela… - Computers & Industrial …, 2025 - Elsevier
Network anomaly detection is essential to promptly detect and fix issues in the network.
Particularly, detecting traffic anomalies enables the early detection of configuration errors …

ChainNet: A Customized Graph Neural Network Model for Loss-Aware Edge AI Service Deployment

Z Niu, M Roveri, G Casale - 2024 54th Annual IEEE/IFIP …, 2024 - ieeexplore.ieee.org
Edge AI seeks for the deployment of deep neural network (DNN) based services across
distributed edge devices, embedding intelligence close to data sources. Due to capacity …

Beyond Normal: Learning Spatial Density Models of Node Mobility

W Gao, I Nikolaidis, J Harms - arxiv preprint arxiv:2411.10997, 2024 - arxiv.org
Learning models of complex spatial density functions, representing the steady-state density
of mobile nodes moving on a two-dimensional terrain, can assist in network design and …

Practical Foreground Traffic Performance Modeling for Wide Area Network

N Gu, Y Si, C Chang, Q Wan, H Yang… - 2024 7th World …, 2024 - ieeexplore.ieee.org
The quality of service (QoS) in wide area networks (WANs) is crucial for ensuring efficient
and reliable communication. Traditional QoS evaluation approaches, such as analysis …