insyt

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distilbert-base-uncased-logline-v3

This model is a fine-tuned version of distilbert-base-uncased on the AIT Log Data Set V2.0 dataset¹, https://zenodo.org/records/5789064. It achieves the following results on the evaluation set:

• Loss: 0.0022
• Accuracy: 0.9995
• F1: 0.9994

Model description

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. 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.

Labels

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Test results

Five Fold Cross Validation Mean Test Confusion Matrix

Framework versions

• Transformers 4.38.2
• Pytorch 2.0.0+cu117
• Datasets 2.18.0
• Tokenizers 0.15.1

Citations

[1]M. Landauer, F. Skopik, M. Frank, W. Hotwagner, M. Wurzenbergerand A. Rauber, “AIT Log Data Set V2.0”. Zenodo, Feb. 24, 2022. doi: 10.5281/zenodo.5789064.