GNN-based Anomaly Detection for Encoded Network Traffic
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909209065422848 |
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| author | Chattopadhyay, Anasuya Reti, Daniel Schotten, Hans D. |
| author_facet | Chattopadhyay, Anasuya Reti, Daniel Schotten, Hans D. |
| contents | The early research report explores the possibility of using Graph Neural Networks (GNNs) for anomaly detection in internet traffic data enriched with information. While recent studies have made significant progress in using GNNs for anomaly detection in finance, multivariate time-series, and biochemistry domains, there is limited research in the context of network flow data. In this report, we explore the idea that leverages information-enriched features extracted from network flow packet data to improve the performance of GNN in anomaly detection. The idea is to utilize feature encoding (binary, numerical, and string) to capture the relationships between the network components, allowing the GNN to learn latent relationships and better identify anomalies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13670 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GNN-based Anomaly Detection for Encoded Network Traffic Chattopadhyay, Anasuya Reti, Daniel Schotten, Hans D. Social and Information Networks Cryptography and Security Machine Learning The early research report explores the possibility of using Graph Neural Networks (GNNs) for anomaly detection in internet traffic data enriched with information. While recent studies have made significant progress in using GNNs for anomaly detection in finance, multivariate time-series, and biochemistry domains, there is limited research in the context of network flow data. In this report, we explore the idea that leverages information-enriched features extracted from network flow packet data to improve the performance of GNN in anomaly detection. The idea is to utilize feature encoding (binary, numerical, and string) to capture the relationships between the network components, allowing the GNN to learn latent relationships and better identify anomalies. |
| title | GNN-based Anomaly Detection for Encoded Network Traffic |
| topic | Social and Information Networks Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2405.13670 |