Top 7 Easy Steps: How to Setup Local Falcon

In the ever-evolving landscape of cybersecurity, staying protected against malicious threats is paramount. One effective solution for enterprises is to implement Falcon, a comprehensive security platform that provides real-time threat intelligence, advanced detection, and automated response capabilities. While Falcon is typically deployed in complex enterprise environments, it is also possible to set up a local instance of Falcon for smaller organizations or specific use cases. Here’s a step-by-step guide to help you set up Local Falcon effortlessly and safeguard your systems.

Prerequisites:

Before delving into the setup process, ensure that your system meets the following requirements:
• A physical server or virtual machine (VM) with a minimum of 2 CPU cores and 8 GB memory.
• Two network interfaces:
• One for internet connectivity, and
• The other for connecting to the devices you wish to protect.
• At least 100 GB of free disk space
• Internet connectivity
• Access to the Falcon Endpoint Protection ISO image

Deployment:

Follow these steps to deploy Local Falcon:
1. Create a new VM or use an existing physical server that meets the prerequisites.
2. Download the Falcon Endpoint Protection ISO image from the CrowdStrike website.
3. Boot the server from the ISO image.
4. Follow the on-screen instructions to install Falcon Endpoint Protection.
5. Choose the “Local Falcon” option during the installation process.
6. Configure the necessary settings such as network interfaces, license information, and device discovery settings.
7. Once the installation is complete, Local Falcon will start automatically.
8. You can now configure your devices to connect to Local Falcon for protection against threats.

Prerequisites for Local Falcon Setup

1. System Requirements

Operating System: macOS 10.15 (Catalina) or later, Ubuntu 18.04 (Bionic Beaver) or later, Windows 10 version 1903 or later
CPU: 4 cores or more
Memory (RAM): 8 GB or more
Storage: 50 GB of free disk space
Virtualization: Hardware virtualization (VT-x or AMD-V) enabled in your BIOS
Docker: Docker Desktop installed (version 20.10.7 or later for macOS and Windows, or version 20.10.12 or later for Ubuntu)

Additional Recommendations

* **CPU:** Intel Core i5-8400 or AMD Ryzen 5 2600 or better
* **Memory (RAM):** 16 GB or more
* **Storage:** 100 GB of free disk space or more
* **Network:** High-speed internet connection

Installing Local Falcon CLI

Requirements

To install and use Local Falcon CLI, you will need the following:

A computer running a supported operating system (Windows, macOS, or Linux)
A stable internet connection
Node.js version 16 or higher
npm version 6 or higher

Installation Instructions

To install Local Falcon CLI, follow these steps:

Open a terminal or command prompt and run the following command:

bash npm install -g @localfalcon/cli

This will install the Local Falcon CLI globally on your system.

Verification

To verify that Local Falcon CLI has been installed successfully, run the following command:

bash localfalcon --version

This should output the version number of the installed CLI.

Additional Installation Options

For more advanced installation options, refer to the official Local Falcon CLI documentation.

Creating a New Project

To initiate a new project in Local Falcon, follow these comprehensive steps:

1. Project Initialization

Launch the Local Falcon Command Line Interface (CLI) using your preferred terminal application.
Utilize the following command: local-falcon init [project-name], where [project-name] represents your desired project name.

2. Project Structure Overview

Upon project creation, Local Falcon will generate a project directory with the following structure:

Directory/File	Description
`experiment.json`	Contains the experiment configuration, including models, hyperparameters, and metrics.
`falcon.yaml`	Specifies the project’s general settings, such as the default execution environment and data handling options.
`input/`	Holds the raw input data for training and evaluation.
`output/`	Stores the experiment’s results, including model checkpoints, training logs, and performance summaries.

3. Setting Up Local Execution Environment (Optional)

Local Falcon allows you to configure a specific execution environment for your project. To do this:
- Navigate to your project’s falcon.yaml file.
- Locate the execution section and modify the relevant settings, such as:
  - environment_type: Choose between “local” for local execution or “remote” for cloud-based execution.
  - local_executor: Specify the local execution mode, such as “docker” or “subprocess”.
  - container_image: Set the Docker container image to use for remote execution.
  For example, to enable Docker-based local execution, you would configure the following:
  
  execution: environment_type: local local_executor: docker container_image: my-custom-image

Installing and Configuring Falcon

Step 1: Installing the Falcon Framework

To install Falcon, run the following command in your terminal:

pip install falcon

Step 2: Creating a Falcon Application

Create a new Python file and add the following code:

from falcon import API

app = API()

@app.route('/')
def index():
    return 'Hello, world!'

Step 3: Configuring the Application

You can configure the Falcon application using the `falcon.App` class. Some common configuration options include:

Option	Description
`cors_enable`	Enable Cross-Origin Resource Sharing (CORS)
`request_type`	Set the request type (e.g., json, form, raw)
`response_type`	Set the response type (e.g., json, html, xml)

Step 4: Routing Requests

In Falcon, you define routes using the `@app.route()` decorator. The decorator takes a URL path as its argument and returns a function that handles the request. The handler function can return any type of data, such as a string, JSON object, or an HTTP status code.

Here is an example of a route handler that returns a JSON response:

@app.route('/data')
def get_data():
    return {'data': ['foo', 'bar', 'baz']}

Additional Routing Options

Method Routing: Specify the HTTP methods that the route handler supports (e.g., @app.route('/data', methods=['GET', 'POST'])).
Param Routing: Capture request parameters using placeholders in the URL path (e.g., @app.route('/user/{user_id}')).
Custom Middleware: Add custom functionality to the request-response pipeline before or after the route handler is executed.

Connecting to a Database

Falcon supports connecting to a variety of databases, including Postgres, MySQL, Oracle, and SQL Server. To connect to a database, you must first configure the database connection settings in the Falcon configuration file. Once the connection settings are configured, you can use Falcon to query and manipulate data in the database.

To configure the database connection settings, open the falcon.conf file and locate the [database] section. In the [database] section, you must specify the following settings:

type: The type of database to connect to. Valid values are postgres, mysql, oracle, and sqlserver.
host: The hostname or IP address of the database server.
port: The port number of the database server.
user: The username to use to connect to the database.
password: The password to use to connect to the database.
database: The name of the database to connect to.

Once the database connection settings are configured, you can use Falcon to query and manipulate data in the database. To query data from the database, you can use the falcon.query() function. The falcon.query() function takes a SQL query as its first argument and returns a list of rows. Each row is a dictionary of column names and values.

To manipulate data in the database, you can use the falcon.execute() function. The falcon.execute() function takes a SQL query as its first argument and executes the query. The falcon.execute() function does not return any value.

Example

The following example shows how to connect to a PostgreSQL database and query data from the database:

import falcon


# Configure the database connection settings.

falcon.config.database.type = 'postgres'

falcon.config.database.host = 'localhost'

falcon.config.database.port = 5432

falcon.config.database.user = 'postgres'

falcon.config.database.password = 'mypassword'

falcon.config.database.database = 'mydatabase'
# Connect to the database.

falcon.connect()
# Query data from the database.

rows = falcon.query("SELECT * FROM users")

# Print the rows. for row in rows: print(row)

Creating Models and Resources

To set up Falcon locally, you’ll need to create models and resources. Here’s how:

1. Create a Model

In the Falcon CLI, run the following command:

“`
falcon model create my_model
“`

This will create a new model called “my_model”.

2. Add Fields to the Model

To add fields to your model, use the following command:

“`
falcon model field create my_model my_field
“`

You can repeat this command to add multiple fields.

3. Create a Resource

To create a resource, use the following command:

“`
falcon resource create my_resource
“`

This will create a new resource called “my_resource”.

4. Associate the Resource with the Model

To associate a resource with a model, use the following command:

“`
falcon resource associate my_resource my_model
“`

This will associate the “my_resource” resource with the “my_model” model.

5. Deploy the Model and Resource

To deploy your model and resource, use the following command:

“`
falcon deploy
“`

This will deploy your model and resource to the Falcon platform.

6. Test the Model and Resource

To test your model and resource, you can use the Falcon CLI or the Falcon web interface. Here’s how to test using the CLI:

Command	Description
falcon predict my_model –input my_input.json	Predict the output of your model using the provided input data.

You can also test your model and resource using the Falcon web interface by navigating to the “Models” or “Resources” tab and clicking on the “Test” button.

Writing HTTP Handlers

HTTP handlers are a crucial aspect of a Falcon web application, allowing you to handle HTTP requests and generate responses. Defining handlers is a straightforward process in Falcon. The following steps outline how to create a basic HTTP handler:

Import the necessary Falcon modules.
Define a class that inherits from falcon.HTTPHandler or falcon.API.
Implement the handle method, which takes a request and response object as arguments.
Write the code to handle the request and generate a response.
Register the handler with the Falcon API object.
Start the API server.

Advanced Handler Configuration

Falcon provides advanced options for customizing HTTP handlers through the use of decorators and configuration settings. These options allow for more complex and sophisticated handling of requests and responses.

Here are some key configuration settings for HTTP handlers:

Setting	Description
bypass_auth	Bypasses any authentication or authorization checks for the handler.
content_type	Specifies the content type of the response.
status	Sets the HTTP status code of the response.
headers	Allows setting custom headers in the response.

Decorators can also be used to enhance HTTP handlers. Some commonly used decorators include:

Decorator	Description
@before	Executes a function before the handle method is called.
@after	Executes a function after the handle method is called.
@on_success	Executes a function when the handle method returns a successful response.
@on_error	Executes a function when the handle method encounters an error.

8. Monitoring Local Falcon Server

Once you have your local Falcon server running, you can monitor its performance using various tools and techniques. Here are some of the most common methods:

Server Logs

Default server logs contain valuable information about the server’s operations, including errors, warnings, and performance metrics. You can access the logs from the Falcon server’s installation directory.

Performance Monitoring Tools

Third-party performance monitoring tools, such as htop or top, provide real-time insights into server resource usage, such as CPU, memory, and network utilization. These tools help you identify performance bottlenecks and optimize the server accordingly.

Cloud Monitoring Services

If you are running your local Falcon server in a cloud environment, you can leverage cloud-native monitoring services. These services offer comprehensive monitoring capabilities, including performance metrics, error tracking, and alerting.

Health Checks

Health checks are automated scripts or tools that regularly check the availability and responsiveness of the server. They can be configured to send alerts if any issues are detected, ensuring prompt attention to potential problems.

Cron Jobs and Scheduling

Cron jobs are scheduled tasks that can be used to automate monitoring operations, such as generating performance reports or checking for specific errors. This approach provides proactive monitoring and reduces the need for manual interventions.

Alerting

Configure alerting mechanisms to receive notifications when predefined performance thresholds are exceeded or when errors occur. Alerts can be sent via email, SMS, or other notification channels, allowing for timely response to critical events.

Dashboarding

Create dashboards to visualize key performance metrics and server status. Dashboards provide an at-a-glance overview of the server’s health and can be customized to display specific metrics relevant to your use case.

Troubleshooting Common Issues

1. Installation Errors

If you encounter installation errors, verify that your system meets the minimum requirements and that you have followed the installation instructions carefully. Consider checking the official documentation for specific error messages.

2. Configuration Errors

Ensure that you have configured your Falcon installation correctly. Check the falcon.conf file and ensure that the settings align with your environment and requirements.

3. Sensor Connection Issues

Confirm that your sensors are properly connected and configured to send data to Falcon. Check network connectivity, sensor settings, and firewall rules to ensure smooth data flow.

4. False Positives and Negatives

Fine-tune your Falcon detection rules to minimize false positives and negatives. Review the detection logic and adjust sensitivity thresholds as needed.

5. Performance Issues

Monitor the performance of Falcon and identify potential bottlenecks. Consider scaling resources, optimizing queries, or adjusting sensor settings to improve performance.

6. Integration Challenges

Ensure that Falcon is properly integrated with your existing systems and applications. Troubleshoot any connectivity or data exchange issues to maintain seamless integration.

7. Licensing Issues

Verify that your Falcon license is valid and active. Ensure that the license key is correct and that it corresponds to the appropriate product and subscription.

8. Update Difficulties

When updating Falcon, follow the recommended procedures and ensure that your system has sufficient resources. Check for any dependencies that may require updates.

9. Advanced Troubleshooting

Consult the official Falcon documentation for detailed troubleshooting guides and advanced troubleshooting procedures. Engage with CrowdStrike support if you encounter persistent or complex issues.

Resource	Description
Falcon Troubleshooting Guide	Official documentation with detailed troubleshooting steps
CrowdStrike Support	Technical assistance from CrowdStrike experts
Falcon Community Forum	User-driven forum for sharing knowledge and troubleshooting strategies

Best Practices for Local Falcon Development

1. Use a Consistent Development Environment

This ensures that your code behaves consistently across different machines and environments.

2. Version Control Your Code

This allows you to track changes, collaborate with others, and recover from mistakes.

3. Use a Dependency Manager

This helps you manage and update the dependencies for your Falcon application.

4. Write Unit Tests

This helps you ensure that your code is functioning as expected.

5. Use a Code Formatter

This helps make your code more readable and consistent.

6. Use a Linter

This helps you identify potential errors and coding style issues.

7. Use a Debugger

This helps you troubleshoot issues in your code.

8. Monitor Your Application’s Performance

This helps you identify and resolve performance issues.

9. Document Your Code

This helps others understand the purpose and functionality of your code.

10. Consider Using a Containerized Development Environment

This provides an isolated and consistent development environment that can be easily scaled and shared with others.

Pros	Cons
Isolated and consistent development environment	Can be complex to set up and manage
Easy to scale and share	May require additional resources

How To Setup Local Falcon

**Setting up Local Falcon**

Requirements:
- Python 3.7 or later
- Docker
- Git
Installation:
- Clone the Local Falcon repository: git clone https://github.com/local-falcon/local-falcon.git
- Install requirements: pip install -r requirements.txt
Configuration:
- Create a .env file in the project directory and add the following variables:
  - LOCAL_FALCON_PORT: The port Local Falcon will listen on
  - LOCAL_FALCON_DEBUG: Set to True to enable debug mode
Running:
- Run Local Falcon: docker-compose up
Access:
- Local Falcon will be accessible at http://localhost:8000/ (or the port specified in .env)

**Tips:**

To stop Local Falcon, run docker-compose down
To restart Local Falcon, run docker-compose restart