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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Network intrusion detection using a hybrid graph-based convolutional network and transformer architecture.

Peter Appiahene¹, Samuel Opoku Berchie¹, Emmanuel Botchway¹

¹Department of Information Technology and Decision Sciences, University of Energy and Natural Resources, Sunyani, Ghana.

|January 21, 2026

Summary

This summary is machine-generated.

A new hybrid intrusion detection model, GConvTrans, effectively identifies sophisticated cyber threats in cloud environments. This advanced system combines graph convolutional and transformer layers for robust network intrusion detection.

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Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

Cloud computing expansion increases vulnerability to advanced cyberattacks.
Traditional security systems struggle against sophisticated intrusions.
Existing AI-based intrusion detection models face limitations with data and dynamic pattern recognition.

Purpose of the Study:

To develop a novel hybrid intrusion detection model for cloud environments.
To address the limitations of current AI models in detecting complex network intrusions.
To enhance network intrusion detection systems (NIDS) using deep learning.

Main Methods:

Proposed a hybrid deep neural network architecture named GConvTrans.
Integrated graph convolutional layers and transformer encoder layers.
Transformed tabular network traffic data into computational graphs using the CIC-IDS 2018 dataset.

Main Results:

GConvTrans achieved high accuracy: 84.7% on training, 96.75% on validation, and 96.94% on testing sets.
The model effectively leveraged local structural information and global context.
Demonstrated the robustness of combining graph learning with deep learning for intrusion detection.

Conclusions:

The GConvTrans model shows significant promise for detecting complex network intrusions in cloud environments.
Combining graph learning and deep learning techniques offers a robust approach to cybersecurity.
Future work includes exploring other datasets, refining the architecture, and analyzing performance on graph learning tasks like link prediction.