# examples

Some ready-2-use/ready-2-extend examples/utils.
All examples are prefixed with their used LANG.

## c-analysed

A feature extractor useful for ML/DL use cases.
It generates CSV files from flow "analyse" events.
Used also by `tests/run_tests.sh` if available.

## c-captured

A capture daemon suitable for low-resource devices.
It saves flows that were guessed/undetected/risky/midstream to a PCAP file for manual analysis.

## c-collectd

A collecd-exec compatible middleware that gathers statistic values from nDPId.

## c-notifyd

A notification daemon that sends information about suspicious flow events to DBUS.

## c-json-stdout

Tiny nDPId json dumper. Does not provide any useful funcationality besides dumping parsed JSON objects.

## c-simple

Integration example that verifies flow timeouts on SIGUSR1.

## cxx-graph

A standalone GLFW/OpenGL application that draws statistical data using ImWeb/ImPlot/ImGui.

## js-rt-analyzer

[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.
Required by `tests/run_tests.sh`

## py-machine-learning

Contains:

1. Classification via Random Forests and SciLearn
2. Anomaly Detection via Autoencoder and Keras (Work-In-Progress!)

Use sklearn together with CSVs created with **c-analysed** to train and predict DPI detections.

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`.

## py-flow-dashboard

A realtime web based graph using Plotly/Dash.
Probably the most informative example.

## py-flow-multiprocess

Simple Python Multiprocess example spawning two worker processes, one connecting to nDPIsrvd and one printing flow id's to STDOUT.

## py-json-stdout

Dump received and parsed JSON objects.

## py-schema-validation

Validate nDPId JSON strings against pre-defined JSON schema's.
See `schema/`.
Required by `tests/run_tests.sh`

## py-semantic-validation

Validate nDPId JSON strings against internal event semantics.
Required by `tests/run_tests.sh`
