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##### simple container just to install laravel dependencies
After cloning the project, execute these commands only one time
```
cd api
cp .env-example .env
docker run --rm -v $(pwd):/app composer install
cd ..
./create_volumes.sh
docker-compose up
docker-compose exec api php artisan migrate:fresh --seed
```
This first docker command just creates an empty container to install the required api dependencies.
The create_volumes script, creates the necessary folder structure and extracts the baseline elasticsearch index.
Finally, the last docker command creates and populates the database.
##### MLTD proof of concept experiment #######
MLTD comes with a model already trained.
The model is trained on the data which where available in TimescaleDB (tables XLSIEM, ADT).
The training data are provided in the csv files "xlsiem.csv" "adt.csv" for results reproduction.
To train a model execute the following POST request with the provided body:
http://localhost:5000/api/v1.0/mltd/training
{
"description":"CUREX data",
"timedb_host":"<the timescaleDB host>",
"timedb_port":5432,
"timedb_username":"<the timescaleDB username>",
"timedb_password":"<the timescaleDB password>",
"timedb_ssl":"False",
"timedb_dbname":"kea",
"asset_id":"server",
"timedb_adt_table":"adt",
"timedb_xlsiem_table":"xlsiem",
"timedb_od_table":"od",
"timedb_measurement":"artificial_events",
"mp_thres_X":10,
"mp_thres_Y":2,
"mp_thres_Z":10,
"mp_pat_length":6,
"rre":"True",
"rfe":"True",
"kofe":"False",
"mil_over":"True",
"fs":"False",
"rf_s":0.06,
"rf_midpoint":"2H",
"hours_before":"4H",
"time_segments":"20T",
"dates":[]
}
In order to obtain the top-k important features, use the following request:
http://127.0.0.1:5000/api/v1.0/mltd/threat-identification/1/<k> #where 1 is the trained model id
##### OD pcap files #######
Incide the OD directory we provide the pcap files used for load testing.
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