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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-07956-w |
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| Summary: | 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 patterns among IP addresses and services, and a Graph Neural Network (GNN) is used to extract structured embeddings. These embeddings are refined through a Transformer-based autoencoder to preserve contextual information, while contrastive learning enforces clear class separation during classification. The system is evaluated on NSL-KDD and CIC-IDS2018 datasets under both binary and multi-class scenarios. Experimental results show an average accuracy of 99.97%, with high precision and recall across all attack types, including minority classes such as U2R and R2L. The model achieves low false-positive rates and demonstrates real-time inference performance with modest resource requirements. Key contributions include an interpretable pipeline using SHAP for feature attribution, a strategy for mitigating class imbalance, and validation across datasets with detailed security and generalizability analyses. These results support the practical applicability of the proposed approach in high-throughput, cloud-based network environments. |
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| ISSN: | 2045-2322 |