T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911788392513536 |
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| author | Belgiovine, Mauro Groen, Joshua Sirera, Miquel Tassie, Chinenye Yıldız, Ayberk Yarkın Trudeau, Sage Ioannidis, Stratis Chowdhury, Kaushik |
| author_facet | Belgiovine, Mauro Groen, Joshua Sirera, Miquel Tassie, Chinenye Yıldız, Ayberk Yarkın Trudeau, Sage Ioannidis, Stratis Chowdhury, Kaushik |
| contents | Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. As this paradigm continues to spread, wireless systems must also evolve to identify active transmitters and unauthorized waveforms in real time under intentional distortion of preambles, extremely low signal-to-noise ratios and challenging channel conditions. We overcome limitations of correlation-based preamble matching methods in such conditions through the design of T-PRIME: a Transformer-based machine learning approach. T-PRIME learns the structural design of transmitted frames through its attention mechanism, looking at sequence patterns that go beyond the preamble alone. The paper makes three contributions: First, it compares Transformer models and demonstrates their superiority over traditional methods and state-of-the-art neural networks. Second, it rigorously analyzes T-PRIME's real-time feasibility on DeepWave's AIR-T platform. Third, it utilizes an extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training, which is released along with the code for community use. Results reveal nearly perfect (i.e. $>98\%$) classification accuracy under simulated scenarios, showing $100\%$ detection improvement over legacy methods in low SNR ranges, $97\%$ classification accuracy for OTA single-protocol transmissions and up to $75\%$ double-protocol classification accuracy in interference scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04837 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge Belgiovine, Mauro Groen, Joshua Sirera, Miquel Tassie, Chinenye Yıldız, Ayberk Yarkın Trudeau, Sage Ioannidis, Stratis Chowdhury, Kaushik Machine Learning Networking and Internet Architecture Systems and Control Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. As this paradigm continues to spread, wireless systems must also evolve to identify active transmitters and unauthorized waveforms in real time under intentional distortion of preambles, extremely low signal-to-noise ratios and challenging channel conditions. We overcome limitations of correlation-based preamble matching methods in such conditions through the design of T-PRIME: a Transformer-based machine learning approach. T-PRIME learns the structural design of transmitted frames through its attention mechanism, looking at sequence patterns that go beyond the preamble alone. The paper makes three contributions: First, it compares Transformer models and demonstrates their superiority over traditional methods and state-of-the-art neural networks. Second, it rigorously analyzes T-PRIME's real-time feasibility on DeepWave's AIR-T platform. Third, it utilizes an extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training, which is released along with the code for community use. Results reveal nearly perfect (i.e. $>98\%$) classification accuracy under simulated scenarios, showing $100\%$ detection improvement over legacy methods in low SNR ranges, $97\%$ classification accuracy for OTA single-protocol transmissions and up to $75\%$ double-protocol classification accuracy in interference scenarios. |
| title | T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge |
| topic | Machine Learning Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2401.04837 |