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Diffstat (limited to 'examples')
| -rw-r--r-- | examples/README.md | 7 | ||||
| m--------- | examples/js-rt-analyzer | 0 | ||||
| m--------- | examples/js-rt-analyzer-frontend | 0 |
3 files changed, 6 insertions, 1 deletions
diff --git a/examples/README.md b/examples/README.md index 5df11d607..2b1d43427 100644 --- a/examples/README.md +++ b/examples/README.md @@ -30,6 +30,10 @@ Integration example that verifies flow timeouts on SIGUSR1. [nDPId-rt-analyzer](https://gitlab.com/verzulli/ndpid-rt-analyzer.git) +## js-rt-analyzer-frontend + +[nDPId-rt-analyzer-frontend](https://gitlab.com/verzulli/ndpid-rt-analyzer-frontend.git) + ## py-flow-info Console friendly, colorful, prettyfied event printer. @@ -44,7 +48,8 @@ Try it with: `./examples/py-machine-learning/sklearn_random_forest.py --csv ./nd 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` and the aformentioned parameters. + +But you can use a [pre-trained model](https://drive.google.com/file/d/1KEwbP-Gx7KJr54wNoa63I56VI4USCAPL/view?usp=sharing) with `--load-model`. ## py-flow-dashboard diff --git a/examples/js-rt-analyzer b/examples/js-rt-analyzer -Subproject 124595114faa31824079f5d709ca04197384318 +Subproject 2ddfa97de7ebd1ceddc9387d83bc66fdbfd60ea diff --git a/examples/js-rt-analyzer-frontend b/examples/js-rt-analyzer-frontend new file mode 160000 +Subproject e2c89fbab138beb4effd729d530ed819782c461 |