1. Mitigating DDoS Attacks on AI Inference Services


    When it comes to deploying AI inference services in the cloud, ensuring the security and availability of these services is of paramount importance. DDoS (Distributed Denial of Service) attacks are a significant threat that can disrupt service operations by overwhelming the infrastructure with traffic. To mitigate such threats, we can utilize specific services and resources provided by cloud providers that specialize in detecting and protecting against DDoS attacks.

    For AWS (Amazon Web Services), a combination of AWS Shield and AWS WAF (Web Application Firewall) can be used to provide comprehensive protection against common web exploits, including DDoS. AWS Shield provides managed DDoS protection that integrates with many AWS services. AWS WAF allows you to monitor web requests directed at your applications and control access to your content.

    Below, we will illustrate a Pulumi program that sets up AWS WAF and AWS Shield for an AI inference service.

    import pulumi import pulumi_aws as aws # Create a Web ACL which dictates what traffic should be allowed or blocked # by defining a set of rules that AWS WAF will apply to the requests. web_acl = aws.wafv2.WebAcl("webAcl", default_action={"allow": {}}, scope="REGIONAL", visibility_config={ "sampled_requests_enabled": True, "cloudwatch_metrics_enabled": True, "metric_name": "aiServiceWAFMetric" }, rules=[ { "name": "RateLimitRule", "priority": 1, "action": {"block": {}}, "statement": { # Rate-based rule to protect against DDoS attacks by limiting # the number of requests from a single IP address within a # five-minute period "rate_based_statement": { "limit": 2000, "aggregate_key_type": "IP" } }, # Visibility config for the rule, in this case, mirroring the Web ACL config "visibility_config": { "sampled_requests_enabled": True, "cloudwatch_metrics_enabled": True, "metric_name": "RateLimitRuleMetric" } } ] ) # Attaching AWS Shield Advanced to protect against more sophisticated and larger DDoS attacks. # Assuming `resource_arn` is the ARN of your AI inference service resource (e.g., an Elastic Load Balancer or an EC2 instance). resource_arn = "arn:aws:elasticloadbalancing:region:account-id:loadbalancer/resource-id" # Check whether Shield Advanced is enabled shield_advanced_protection = aws.shield.Protection("shieldAdvancedProtection", resource_arn=resource_arn # Tags could be added to organize and manage costs ) # Export the ID of the Web ACL and the Shield Advanced Protection pulumi.export("web_acl_id", web_acl.id) pulumi.export("shield_advanced_protection_id", shield_advanced_protection.id)

    In this Pulumi program:

    1. We create a WebAcl which is essentially a set of rules that tell AWS WAF how to manage traffic. The ACL includes a rate-based blocking rule that automatically blocks any IP address that sends more than 2000 requests in a five-minute period, which can reduce the impact of a DDoS attack by limiting the number of requests one source can make.

    2. We then create a Protection resource with AWS Shield Advanced, attaching it to your AI inference service's resource ARN. Replace resource_arn with the actual ARN of your service resource. AWS Shield Advanced provides additional protections against DDoS attacks and integrates with AWS WAF for enhanced security posture.

    Lastly, we export the Web ACL ID and the Shield Protection ID using pulumi.export which can be used as references for other parts of your infrastructure or for audit purposes. This program needs to be adjusted with the actual ARN from your AI service, and you should also set the rate limit according to your specific traffic patterns and requirements.

    For more details on configuring AWS WAF and AWS Shield, you can refer to the following documentation:

    Remember, every infrastructure is unique, so make sure to tailor the rules and configurations to suit your specific use case.