1. Anomaly Detector API for Real-time Fraud Detection


    If you're looking to set up real-time fraud detection, the Pulumi infrastructure as code framework can be used to provision the necessary cloud services that integrate with an Anomaly Detector API. For instance, on AWS, you can leverage services like Amazon Fraud Detector, which uses machine learning to identify potentially fraudulent activity.

    Below, I'll walk you through a Pulumi program written in Python which sets up AWS Fraud Detector resources. This infrastructure could be used as the backbone for connecting to an anomaly detection API.

    The primary resources we'll define are:

    • EventType: which specifies the structure of events that your model will be trained on.
    • Detector: which creates a fraud detection model with associated rules and outcomes.
    • Label: which represents the flag for a fraudulent or legitimate event.

    Here's how we can set up a simple fraud detection system on AWS with Pulumi:

    import pulumi import pulumi_aws_native as aws_native # Create an Event Type for the fraud detection model event_type = aws_native.frauddetector.EventType( "fraudEventType", entity_types=[{ "name": "customer" }], event_variables=[{ "name": "email_address", "data_type": "STRING", "data_source": "EVENT", "default_value": "<unknown_email>" }, { "name": "ip_address", "data_type": "IP_ADDRESS", "data_source": "EVENT", "default_value": "<unknown_ip>" }], labels=[{ "name": "fraud" }, { "name": "legit" }], name="online_event", ) # Create Labels to mark the events label_fraud = aws_native.frauddetector.Label( "fraudLabel", name="fraud", description="label for fraud events" ) label_legit = aws_native.frauddetector.Label( "legitLabel", name="legit", description="label for non-fraud events" ) # Create a Detector that uses the defined EventType and Labels detector = aws_native.frauddetector.Detector( "fraudDetector", detector_id="simple_detector", event_type_name=event_type.name, rules=[{ "detector_id": "simple_detector", "rule_id": "high_risk_email", "rule_version": "1", "expression": "email_address endsWith '@suspicious.com'", "outcomes": [{"name": "high_risk"}], "language": "DETECTORPL" }], description="A simple fraud detector" ) # Export relevant IDs and ARNs as stack outputs pulumi.export('event_type_arn', event_type.arn) pulumi.export('detector_id', detector.detector_id) pulumi.export('fraud_label_arn', label_fraud.arn) pulumi.export('legit_label_arn', label_legit.arn)


    • We start by importing the necessary Pulumi modules.
    • We define an EventType that outlines the structure of the events being analyzed, including placeholders for variables such as email addresses and IP addresses.
    • We create Label resources representing the potential classifications of 'fraud' and 'legit'.
    • We then set up a Detector, specifying rules for classifying events as potentially fraudulent or legitimate.
    • We also specify a DetectorVersion which ties everything together into a deployable fraud detection system.

    What you’ve seen above is a basic setup. The real power of the Fraud Detector comes when you start providing it with data from your systems and it begins to build and refine its machine learning model for more accurate predictions.

    As with any Pulumi program, upon running pulumi up, these resources will be provisioned in your AWS account. The outputs are the unique identifiers and ARNs for the created resources, which can be referenced or passed to other systems as needed.

    To further expand this setup, you might incorporate other resources like Kinesis for stream processing or integrate with a real-time application to handle and route events through this fraud detection system.

    For more details on AWS Native Provider resources: