Building a Real-Time ML Cyber Attack Detection | Autoencoder, ORC, SGD & AWS CI/CD Cloud Formation

In this advanced, full-length tutorial, we’ll go beyond “just deploying an ML model” we’ll design, train, and deploy a production-grade, real-time cyber attack detection system with full automation on AWS. 🔗 Full code, templates, and docs in the GitHub repo (link below): https://github.com/samugit83/TheGradientPath/tree/master/RealWorldProjects/CyberAttackPrediction From data preprocessing to CI/CD pipelines, we’ll explore every moving part of the system, explaining not only what to do but also why including the math and theory behind the machine learning components. You’ll learn how to: 🧠 Machine Learning Core Engineer network traffic features (packet counts, byte sizes, protocol types, connection states) Apply robust scaling & log transforms to neutralize outliers without distorting normal patterns Train an autoencoder to model normal network behavior & detect anomalies via reconstruction error Implement ORC (Online Reconstruction-based Selection) for continuous, real-time feature importance tracking Train an SGD classifier with incremental learning to classify attacks from streaming data Handle concept drift and class imbalance in evolving traffic patterns 🔄 Dual Training Pipelines Batch mode — for historical datasets, full statistical analysis, and optimal hyperparameter tuning Streaming mode — for live incremental learning from packet captures, adapting in real time ☁ AWS Infrastructure & Deployment CloudFormation — provision EC2, Auto Scaling Groups, Load Balancer, security monitoring agents AWS CodePipeline + CodeBuild + CodeDeploy — automate the entire build/test/deploy process Blue/Green deployments — achieve zero downtime updates with dual Auto Scaling Groups ⚙ Deployment Scripts in Action We’ll dissect 7 production-hardened lifecycle scripts that make deployments rock-solid: before_install.sh — clean environment install.sh — OS & dependency setup after_install.sh — environment configuration & Python/Node.js setup stop_app.sh — gra