Starweaver

DevSecOps & Cloud Security

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Starweaver

DevSecOps & Cloud Security

Sergii Demianchuk
Starweaver

位教师:Sergii Demianchuk

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
高级设置 等级

推荐体验

8 小时 完成
灵活的计划
自行安排学习进度
深入了解一个主题并学习基础知识。
高级设置 等级

推荐体验

8 小时 完成
灵活的计划
自行安排学习进度

您将学到什么

  • Analyze the AI bot threat landscape and set up a local Flask application with Terraform tooling.

  • Deploy production AWS infrastructure with Terraform: VPC, ALB, CloudFront, WAF, and EC2 auto scaling groups.

  • Implement intelligent traffic routing, cache separation, and degraded content with CloudFront and Lambda@Edge.

  • Configure advanced WAF protections, analyze logs with Athena, and enforce a data-driven strategic bot policy.

要了解的详细信息

可分享的证书

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最近已更新!

April 2026

作业

4 项作业

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有4个模块

This module explores the economic and technical forces behind the AI bot surge using real traffic data from a commercial marketplace. Learners will examine how training and on-demand bots differ in their impact, why traditional defenses are no longer sufficient, and what a high-level multi-layered infrastructure strategy looks like. The module then transitions to hands-on preparation, where learners set up the Flask demo application locally, install Terraform, configure AWS credentials, and push the Docker image to ECR, establishing the prerequisites for cloud deployment in subsequent modules.

涵盖的内容

9个视频2篇阅读材料1个作业1次同伴评审1个讨论话题

This module moves from local development to full cloud deployment using Terraform. Learners will build a VPC with public and private subnets, configure security groups, delegate a domain to Route 53, generate SSL certificates with ACM, deploy an Application Load Balancer, and launch EC2 instances in an Auto Scaling group running the Flask container. The module then layers on CloudFront and WAF with logging via Kinesis Firehose. It concludes with a practical analysis of why reactive auto-scaling fails against short, aggressive AI bot spikes, using real commercial data from a Petalbot and Ahrefsbot traffic event.

涵盖的内容

14个视频1篇阅读材料1个作业1次同伴评审1个讨论话题

This module transforms CloudFront into a bot-aware traffic router. Learners will understand the two-layer caching architecture of CloudFront, implement a degraded content strategy that serves lightweight static content to bots while preserving the full experience for humans, and deploy Lambda@Edge to dynamically route bot traffic to a secondary CloudFront distribution backed by S3. The module also addresses cache collision between bot and human responses using CloudFront Functions, solves the missing assets problem through immutable asset deployments and Origin Shield, and concludes with a summary of all key patterns for bot-resilient content delivery.

涵盖的内容

13个视频1篇阅读材料1个作业1次同伴评审1个讨论话题

This module turns the CloudFront edge into an intelligent security gateway. Learners will deploy AWS WAF with IP black and white lists, GEO-based country blocking with whitelist exceptions, and set up Athena to query WAF logs for bot geography analysis. The module progresses through JA4 TLS fingerprinting for advanced rate limiting, granular URL-scoped rate rules, and the AWS IP Reputation List managed rule group. Learners will then enable AWS WAF Bot Control in COMMON mode, examine the labels and categories it emits, integrate the client-side SDK to unlock TARGETED mode, and interpret the Bot Control dashboards. The module culminates in building a Bot Identification Report using Athena and implementing a fully automated three-tier policy (allow, block, degrade) via Lambda@Edge and WAF enforcement rules.

涵盖的内容

15个视频1篇阅读材料1个作业2次同伴评审1个讨论话题

位教师

Sergii Demianchuk
Starweaver
1 门课程1 名学生

提供方

Starweaver

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