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# distilbert-base-uncased-logline-v3
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the AIT Log Data Set V2.0 dataset<sup>1</sup
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It achieves the following results on the evaluation set:
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- Loss: 0.0022
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- Accuracy: 0.9995
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## Model description
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This model is meant for text classification of log files for network intrusion detection. The python package that runs this model can be found here -> https://github.com/Isaacwilliam4/INSyT.
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## Labels
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| Label | Label Name |
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# distilbert-base-uncased-logline-v3
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the AIT Log Data Set V2.0 dataset<sup>1</sup>, https://zenodo.org/records/5789064.
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It achieves the following results on the evaluation set:
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- Loss: 0.0022
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- Accuracy: 0.9995
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## Model description
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This model is meant for text classification of log files for network intrusion detection. The python package that runs this model can be found here -> https://github.com/Isaacwilliam4/INSyT.
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As mentioned on their site, this model was trained on the following logs: Apache access and error logs, authentication logs, DNS logs, VPN logs, audit logs, Suricata logs, network traffic packet captures, horde logs, exim logs, syslog, and system monitoring logs.
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## Labels
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| Label | Label Name |
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