1. Mitigating DDoS on AI Inference Services


    Mitigating distributed denial-of-service (DDoS) attacks on AI inference services is crucial for maintaining service availability and performance. Azure provides various services and features that can help protect your AI inference services against DDoS attacks.

    The key steps in mitigating DDoS involve setting up your infrastructure to be resilient, using services that have built-in DDoS mitigation features, and configuring those features to suit your needs. Below is a Pulumi program in Python that demonstrates how to create an Azure Machine Learning inference endpoint with a network security group and DDoS protection plan to mitigate DDoS attacks.

    In this program, we will:

    1. Create an Azure Resource Group to organize all our related resources.
    2. Create a Network Security Group to control the inbound and outbound network traffic to Azure resources.
    3. Create a public IP with a DDoS protection plan enabled.
    4. Deploy an Azure Machine Learning Workspace as a foundational service for all machine learning activities.
    5. Set up an Azure Machine Learning Inference endpoint which is essentially a web service that can receive scoring requests and return predictions.

    Before running this program, make sure to have the Azure provider configured with the necessary credentials.

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import machinelearningservices as ml # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('resource_group') # Create a Network Security Group for the inference endpoint nsg = azure_native.network.NetworkSecurityGroup( 'nsg', resource_group_name=resource_group.name, location=resource_group.location, ) # Create a DDoS Protection Plan ddos_protection_plan = azure_native.network.DdosProtectionPlan( 'ddos_protection_plan', location=resource_group.location, resource_group_name=resource_group.name, ) # Create a public IP address with DDoS protection enabled public_ip = azure_native.network.PublicIPAddress( 'public_ip', resource_group_name=resource_group.name, location=resource_group.location, public_ip_allocation_method=azure_native.network.IPAllocationMethod.STATIC, ddos_protection_plan=azure_native.network.SubResource( id=ddos_protection_plan.id, ), ) # Create an Azure Machine Learning Workspace ml_workspace = ml.Workspace( 'ml_workspace', resource_group_name=resource_group.name, location=resource_group.location, ) # Create an Azure Machine Learning Inference Endpoint inference_endpoint = ml.InferenceEndpoint( 'inference_endpoint', sku=ml.SkuArgs( name='Standard_F2s_v2' ), location=ml_workspace.location, resource_group_name=resource_group.name, workspace_name=ml_workspace.name, inference_endpoint_properties=ml.InferenceEndpointPropertiesArgs( # Properties for your inference endpoint ), ) pulumi.export('inference_endpoint_url', inference_endpoint.properties.apply(lambda props: props.fqdn))

    In the code above:

    • We created a NetworkSecurityGroup to provide a barrier between the internet and the Azure resources, which can filter traffic based on rules.
    • We set up a DdosProtectionPlan and a PublicIPAddress with DDoS protection. This public IP will be associated with the Azure Machine Learning Service's Inference Endpoint, hence protecting it from DDoS attacks.
    • We used the InferenceEndpoint resource from the machinelearningservices module of Azure Native Pulumi provider to deploy an inference service where AI models can be hosted.

    Keep in mind that for a complete DDoS mitigation implementation, you would need to define specific network security rules within the NetworkSecurityGroup, such as allowing only essential ports and IP addresses. Additionally, you would also adopt best practices in your application code such as rate limiting, request validation, and proper error handling.

    Remember to export the FQDN (fully qualified domain name) of the inference endpoint so that it can be accessed by clients to make predictions.

    Run this Pulumi program by saving it to a file (e.g., ddos_mitigation.py) and executing pulumi up in your command line from the same directory as the file. This command will build the infrastructure described by your program in the cloud provider, which in this case is Azure.