Unsupervised Network Anomaly Detection with Autoencoders and Traffic Images
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915660497420288 |
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| author | Neri, Michael Baldoni, Sara |
| author_facet | Neri, Michael Baldoni, Sara |
| contents | Due to the recent increase in the number of connected devices, the need to promptly detect security issues is emerging. Moreover, the high number of communication flows creates the necessity of processing huge amounts of data. Furthermore, the connected devices are heterogeneous in nature, having different computational capacities. For this reason, in this work we propose an image-based representation of network traffic which allows to realize a compact summary of the current network conditions with 1-second time windows. The proposed representation highlights the presence of anomalies thus reducing the need for complex processing architectures. Finally, we present an unsupervised learning approach which effectively detects the presence of anomalies. The code and the dataset are available at https://github.com/michaelneri/image-based-network-traffic-anomaly-detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Network Anomaly Detection with Autoencoders and Traffic Images Neri, Michael Baldoni, Sara Computer Vision and Pattern Recognition Cryptography and Security Image and Video Processing Signal Processing Due to the recent increase in the number of connected devices, the need to promptly detect security issues is emerging. Moreover, the high number of communication flows creates the necessity of processing huge amounts of data. Furthermore, the connected devices are heterogeneous in nature, having different computational capacities. For this reason, in this work we propose an image-based representation of network traffic which allows to realize a compact summary of the current network conditions with 1-second time windows. The proposed representation highlights the presence of anomalies thus reducing the need for complex processing architectures. Finally, we present an unsupervised learning approach which effectively detects the presence of anomalies. The code and the dataset are available at https://github.com/michaelneri/image-based-network-traffic-anomaly-detection. |
| title | Unsupervised Network Anomaly Detection with Autoencoders and Traffic Images |
| topic | Computer Vision and Pattern Recognition Cryptography and Security Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2505.16650 |