NetNN: Neural Intrusion Detection System in Programmable Networks
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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_ | 1866914851145646080 |
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| author | Razavi, Kamran Fard, Shayan Davari Karlos, George Nigade, Vinod Mühlhäuser, Max Wang, Lin |
| author_facet | Razavi, Kamran Fard, Shayan Davari Karlos, George Nigade, Vinod Mühlhäuser, Max Wang, Lin |
| contents | The rise of deep learning has led to various successful attempts to apply deep neural networks (DNNs) for important networking tasks such as intrusion detection. Yet, running DNNs in the network control plane, as typically done in existing proposals, suffers from high latency that impedes the practicality of such approaches. This paper introduces NetNN, a novel DNN-based intrusion detection system that runs completely in the network data plane to achieve low latency. NetNN adopts raw packet information as input, avoiding complicated feature engineering. NetNN mimics the DNN dataflow execution by mapping DNN parts to a network of programmable switches, executing partial DNN computations on individual switches, and generating packets carrying intermediate execution results between these switches. We implement NetNN in P4 and demonstrate the feasibility of such an approach. Experimental results show that NetNN can improve the intrusion detection accuracy to 99\% while meeting the real-time requirement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NetNN: Neural Intrusion Detection System in Programmable Networks Razavi, Kamran Fard, Shayan Davari Karlos, George Nigade, Vinod Mühlhäuser, Max Wang, Lin Cryptography and Security Distributed, Parallel, and Cluster Computing The rise of deep learning has led to various successful attempts to apply deep neural networks (DNNs) for important networking tasks such as intrusion detection. Yet, running DNNs in the network control plane, as typically done in existing proposals, suffers from high latency that impedes the practicality of such approaches. This paper introduces NetNN, a novel DNN-based intrusion detection system that runs completely in the network data plane to achieve low latency. NetNN adopts raw packet information as input, avoiding complicated feature engineering. NetNN mimics the DNN dataflow execution by mapping DNN parts to a network of programmable switches, executing partial DNN computations on individual switches, and generating packets carrying intermediate execution results between these switches. We implement NetNN in P4 and demonstrate the feasibility of such an approach. Experimental results show that NetNN can improve the intrusion detection accuracy to 99\% while meeting the real-time requirement. |
| title | NetNN: Neural Intrusion Detection System in Programmable Networks |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2406.19990 |