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diff --git a/examples/README.md b/examples/README.md index 71b7b8204..eb00539d9 100644 --- a/examples/README.md +++ b/examples/README.md @@ -37,15 +37,12 @@ Prints prettyfied information about flow events. Use sklearn together with CSVs created with **c-analysed** to train and predict DPI detections. -Try it with: `./examples/py-machine-learning/sklearn-ml.py --csv ./ndpi-analysed.csv --proto-class tls.youtube --proto-class tls.github --proto-class tls.spotify --proto-class tls.facebook --proto-class tls.instagram --proto-class tls.doh_dot --proto-class quic --proto-class icmp` +Try it with: `./examples/py-machine-learning/sklearn_random_forest.py --csv ./ndpi-analysed.csv --proto-class tls.youtube --proto-class tls.github --proto-class tls.spotify --proto-class tls.facebook --proto-class tls.instagram --proto-class tls.doh_dot --proto-class quic --proto-class icmp` This way you should get 9 different classification classes. You may notice that some classes e.g. TLS protocol classifications may have a higher false-negative rate. - Unfortunately, I can not provide any datasets due to some privacy concerns. -But you can use a [pre-trained model](https://drive.google.com/file/d/1KEwbP-Gx7KJr54wNoa63I56VI4USCAPL/view?usp=sharing) with `--load-model` using python-joblib. -Please send me your CSV files to improve the model. I will treat those files confidential. -They'll only be used for the training process and purged afterwards. +But you can use a [pre-trained model](https://drive.google.com/file/d/1KEwbP-Gx7KJr54wNoa63I56VI4USCAPL/view?usp=sharing) with `--load-model` and the aformentioned parameters. ## py-flow-dashboard |