Activating logs, metrics, and monitoring around edge services to indicate attacks

Can you describe the process of enabling logging for AWS CloudFront and what type of security insights it can provide?

To enable logging for AWS CloudFront, you would navigate to the CloudFront Distribution settings and enable the option for creating access logs. These access logs contain details about each request made to the CloudFront distribution, including viewer IP address, requested URI, response code, and user agent. These insights help in identifying patterns indicative of attacks such as DDoS or targeted attempts to breach security.

How does AWS WAF help in protecting your edge services and what type of monitoring capabilities does it include?

AWS WAF is a web application firewall that helps protect edge services by allowing you to set conditions to block, allow, or monitor (count) web requests based on IP addresses, HTTP headers, HTTP body, or URI strings. Its monitoring capabilities include real-time metrics and sampled requests, which are delivered to Amazon CloudWatch for further analysis, enabling detection of possible attacks.

What are the benefits of using AWS Shield, especially with regards to attack detection and reporting?

AWS Shield is a managed Distributed Denial of Service (DDoS) protection service that safeguards applications running on AWS. AWS Shield Standard protects against most common, frequently occurring types of DDoS attacks. AWS Shield Advanced provides enhanced protection with detailed attack diagnostics. Users benefit from near real-time visibility into attacks, extensive reporting, and DDoS cost protection.

How can Amazon CloudWatch be utilized to monitor for potential security threats against your edge services?

Amazon CloudWatch can be utilized by configuring alarms based on metrics from AWS services (like CloudFront and WAF) to monitor for thresholds indicative of potential security threats. For example, you can set alarms for high numbers of 4XX or 5XX status codes, or a surge in traffic, which might suggest a DDoS attack or other malicious activity.

Explain how AWS Lambda functions can be used to enhance real-time security monitoring around edge services?

AWS Lambda can be triggered by Amazon CloudWatch Logs and CloudFront real-time log delivery. Once triggered, it can run custom codes to filter and analyze logs from edge services for indications of suspicious activities. Lambda functions can also integrate with other AWS services, like sending alerts through Amazon SNS or modifying AWS WAF rules in response to certain triggers.

In what ways does Amazon GuardDuty provide monitoring and threat detection for your AWS environment, and how does it apply to edge services?

Amazon GuardDuty offers intelligent threat detection for your AWS environment by continuously analyzing AWS CloudTrail events, VPC flow logs, and DNS logs. While GuardDuty is not specifically tailored for edge services, its ability to detect unusual API calls or potentially unauthorized deployments that could affect the security status of your edge services is invaluable.

Describe the role that AWS CloudTrail plays in security monitoring and the investigation of incidents related to edge services.

AWS CloudTrail logs all API calls made to AWS services, including those related to edge services configuration and management. By analyzing CloudTrail logs, security teams can audit changes to their environment, investigate the source of modifications, detect unusual activity, and ensure compliance with governance policies.

Can you explain how to integrate AWS edge services logging with SIEM (Security Information and Event Management) systems for advanced threat detection?

To integrate AWS edge services logging with SIEM systems, you’d typically export logs from services like CloudFront, WAF, and CloudTrail to a centralized storage solution like Amazon S From there, logs can be ingested into the SIEM using its built-in connectors or through custom log processing pipelines, enabling advanced correlation, alerting, and forensic capabilities.

How can machine learning be employed to identify potential attacks in the logs and metrics of edge services?

Machine learning tools, such as Amazon SageMaker, can analyze large volumes of log and metric data to identify patterns and anomalies that may signify attacks. By training machine learning models with historical data, it’s possible to forecast and flag unusual behaviors, auto-adjust security postures, and potentially stop attacks in real-time.

What type of automated response mechanisms can you set up using AWS services when a potential attack is detected on edge services?

AWS supports automated response mechanisms through services like AWS Lambda, SNS, and Step Functions. For example, Lambda can automatically modify WAF rules in response to detected threats, while SNS can send out notifications. Step Functions can orchestrate complex workflows, like initiating forensic analysis or executing predefined mitigation strategies.