Auto Reconnaissance

Description

This is a sample application showcasing how to use Lattice HTTP SDKs to perform Entity Auto Reconnaissance.

The program streams all incoming entities with the Entities API, determines if there is any non-friendly track within a certain distance from an asset. If this requirement is fulfilled, the auto reconnaissance system classifies the track disposition as suspicious and creates an investigation task for the asset to investigate the track. You will create a pair of a simulated asset and a track for a clear demonstration of this process.

The following endpoints are showcased in this application:

• the long_poll_entity_events Entities API endpoint to long poll for incoming entities.
• the publish_entity_rest Entities API endpoint to publish entities.
• the put_entity_override_rest Entities API endpoint to override certain entity fields.
• the create_task Tasks API endpoint to create new tasks.
• the get_task_by_id Tasks API endpoint to retrieve tasks.
• the long_poll_listen_as_agent Tasks API endpoint to listen as an agent.
• the update_task_status_by_id Tasks API endpoint to update a task's status.

How to run locally

Prerequisites

• Python version greater than or equal to 3.9

Before you begin

Ensure you have set up your development environment

Clone the repository

git clone https://github.com/anduril/sample-app-auto-reconnaissance.git sample-app-auto-reconnaissance
cd sample-app-auto-reconnaissance

Optional: Initialize a virtual environment

python -m venv .venv
source .venv/bin/activate

Install dependencies and configure project

Follow the guide here to generate your Python HTTP SDK.

1. Navigate to the requirements.txt file and change the path to the SDKs according to where you have outputted the entities_api and tasks_api packages. After updating these paths, run the following command:

pip install -r requirements.txt

2. Modify the configuration files for the auto reconnaissance system in auto-reconnaissance/var/config.yml, the simulated asset in simulated_asset/var/config.yml, and the simulated track in simulated_track/var/config.yml.

• Replace <YOUR_LATTICE_IP> and <YOUR_LATTICE_BEARER_TOKEN> with your Lattice IP and Lattice Bearer Token

lattice-ip: <YOUR_LATTICE_IP>
lattice-bearer-token: <YOUR_LATTICE_BEARER_TOKEN>

• If you would like to change the latitude and longitude of your simulated asset and track, you can do so in the corresponding config files. The default distance threshold for the auto reconnaissance system is 5 miles. Ensure that the latitude and longitude inputs for your asset and track are within this distance.

latitude: <YOUR_LATITUDE>
longitude: <YOUR_LONGITUDE>

Run the program

Open separate terminals to run the following commands. If you are using a virtual environment, ensure that the virtual environment is activated for all terminals.

python auto-reconnaissance/main.py --config auto-reconnaissance/var/config.yml

python simulated_asset/asset.py --config simulated_asset/var/config.yml

python simulated_track/track.py --config simulated_track/var/config.yml

Navigate to your Lattice UI and observe the Active Tasks tab. When assets come within range of a non-friendly track, an investigation task will be created. If you observe the simulated asset and track, you will see that the auto reconnaissance system will classify the track disposition as suspicious, and a task will be created for the asset to investigate the track.

On the console, you will see the auto reconnaissance system creating a task:

INFO:EARS:ASSET WITHIN RANGE OF NON-FRIENDLY TRACK
INFO:EARS:overriding disposition for track $ENTITY_ID
INFO:EARS:Task created - view Lattice UI, task id is $TASK_ID

Simultaneously, you will see the simulated asset receive the execute request:

INFO:SIMASSET:received execute request, sending execute confirmation

Afterwards, the auto reconnaissance system will continuously check the status of any tasks being executed.

Here is a screenshot of this in action:

Congrats, you've tasked an asset to investigate a track!