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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.10624 |
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| _version_ | 1866910830301282304 |
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| author | Chen, Kehua Jia, Jingping |
| author_facet | Chen, Kehua Jia, Jingping |
| contents | Network evasion detection aims to distinguish whether the network flow comes from link layer exists network evasion threat, which is a means to disguise the data traffic on detection system by confusing the signature. Since the previous research works has all sorts of frauds, we propose a architecture with deep learning network to handle this problem. In this paper, we extract the critical information as key features from data frame and also specifically propose to use bidirectional long short-term memory (Bi-LSTM) neural network which shows an outstanding performance to trace the serial information, to encode both the past and future trait on the network flows. Furthermore we introduce a classifier named Softmax at the bottom of Bi-LSTM, holding a character to select the correct class. All experiments results shows that we can achieve a significant performance with a deep Bi-LSTM in network evasion detection and it's average accuracy reaches 96.1%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Network evasion detection with Bi-LSTM model Chen, Kehua Jia, Jingping Cryptography and Security Artificial Intelligence Network evasion detection aims to distinguish whether the network flow comes from link layer exists network evasion threat, which is a means to disguise the data traffic on detection system by confusing the signature. Since the previous research works has all sorts of frauds, we propose a architecture with deep learning network to handle this problem. In this paper, we extract the critical information as key features from data frame and also specifically propose to use bidirectional long short-term memory (Bi-LSTM) neural network which shows an outstanding performance to trace the serial information, to encode both the past and future trait on the network flows. Furthermore we introduce a classifier named Softmax at the bottom of Bi-LSTM, holding a character to select the correct class. All experiments results shows that we can achieve a significant performance with a deep Bi-LSTM in network evasion detection and it's average accuracy reaches 96.1%. |
| title | Network evasion detection with Bi-LSTM model |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2502.10624 |