Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning
Abstract This paper presents a modular and scalable intrusion detection framework that combines graph-based feature extraction, Transformer-based autoencoding, and contrastive learning to improve detection accuracy in cloud environments. Network flows are modeled as graphs to capture relational patt...
Saved in:
| Main Authors: | Vijay Govindarajan, Junaid Hussain Muzamal |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-07956-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Graph-contrast ransomware detection (GCRD) with advanced feature selection and deep learning
by: Suneeta Satpathy, et al.
Published: (2025-06-01) -
Multi-View Intrusion Detection Framework Using Deep Learning and Knowledge Graphs
by: Min Li, et al.
Published: (2025-05-01) -
A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT
by: Li Ma, et al.
Published: (2025-12-01) -
Advanced intrusion detection in internet of things using graph attention networks
by: Aamir S. Ahanger, et al.
Published: (2025-03-01) -
Implementation of Intrusion detection and prevention with Deep Learning in Cloud Computing
by: Doddi Srilatha, et al.
Published: (2023-01-01)