Real-time Anomaly Detection Notifications with AWS SNS

Question

Pulumi · Accepted Answer

To accomplish real-time anomaly detection with notifications in AWS, you would typically use Amazon CloudWatch for monitoring and anomaly detection, and AWS Simple Notification Service (SNS) for sending the notifications.

Here's the overall process:
1. **CloudWatch Alarms**: You create a CloudWatch Alarm to monitor a specific metric. CloudWatch supports anomaly detection on metrics, which applies machine learning to determine what is normal and then triggers an alarm when anomalies are detected.
2. **SNS Topic**: You create an SNS Topic, which is a channel that the alarm messages can be sent through.
3. **Alarm Action**: You configure the alarm to publish a message to the SNS Topic when the alarm state changes (e.g., when an anomaly is detected).
4. **SNS Subscriptions**: You create subscriptions for the SNS Topic, defining how the notifications should be sent out, for example via email, SMS, or another supported protocol.

Let's implement this with Pulumi in Python:

```python
import pulumi
import pulumi_aws as aws

# Step 1: Create an SNS Topic to send notifications
sns_topic = aws.sns.Topic("anomaly_detection",
    name="anomaly-detection-topic")

# This could be any supported endpoint such as an email address, SMS number, AWS Lambda Function, etc.
# In this case, we're using an email address as the endpoint for simplicity.
sns_subscription = aws.sns.TopicSubscription("anomaly_detection_subscription",
    topic=sns_topic.arn,
    protocol="email",
    endpoint="example@email.com") # Replace with your email

# Step 2: Create a CloudWatch Metric Alarm for anomaly detection
# This part is highly specific to what you're monitoring.
# Below is a hypothetical example that monitors the CPU Utilization of an EC2 instance.
anomaly_detection_alarm = aws.cloudwatch.MetricAlarm("anomaly_detection_alarm",
    comparison_operator="GreaterThanThreshold",
    evaluation_periods=1, # Number of periods to evaluate for the threshold
    metric_name="CPUUtilization",
    namespace="AWS/EC2",  # Namespace for the metric
    period=300,  # Time in seconds over which the specified statistic is applied
    statistic="Average",
    threshold=80,  # The value against which the specified statistic is compared
    alarm_description="Alarm when server CPU exceeds 80%",
    dimensions={"InstanceId": "i-1234567890abcdef0"},  # Replace with your actual instance ID
    # Using the alarm action to send a message to the SNS Topic
    alarm_actions=[sns_topic.arn])

# Export the SNS Topic ARN and CloudWatch Alarm ID as stack outputs
pulumi.export("sns_topic_arn", sns_topic.arn)
pulumi.export("cloudwatch_alarm_id", anomaly_detection_alarm.id)
```

In this code, we are accomplishing the following:
- Creating an SNS Topic for sending the anomaly notification.
- Subscribing an email endpoint to the SNS Topic for alerting purposes.
- Creating a CloudWatch Metric Alarm for detecting anomalies in CPU utilization of an EC2 instance. When the CPU utilization exceeds 80%, an alarm will be triggered.
- Once the alarm is triggered, it will publish a message to the SNS Topic.
- Exporting the created SNS Topic ARN and the CloudWatch Alarm ID for reference or use in other applications.

Remember to replace `"i-1234567890abcdef0"` with your AWS EC2 instance ID and `example@email.com` with the endpoint where you wish to receive notifications.

The above Pulumi program serves as a basic template for anomaly detection with notifications, and you can customize it to monitor other AWS resources or metrics as needed.