Machine Learning for Cybersecurity: AI Threat Detection

About this course

This course contains the use of artificial intelligence.

Traditional static firewall rules break the moment an attacker changes a single port or byte, flooding Security Operations Center (SOC) teams with thousands of false alarms. In this hands-on course, you will build, test, and deploy a complete, end-to-end Machine Learning Threat Detection and Intrusion Detection System from scratch in Python.

You will engineer a production-grade security pipeline using Python, scikit-learn, FastAPI, Pydantic, Streamlit, and pytest that runs 100% locally on any standard computer:

Zero-Leakage Telemetry Preprocessing: Generate reproducible enterprise host and network logs and scale 6 core security features (CPU, Memory, Network Bytes, Logins, File Writes, and Connections) using a Standard

Scaler fit strictly on training splits.

Unsupervised Zero-Day Anomaly Detection: Train a 200-tree Isolation Forest exclusively on clean baseline traffic to isolate novel, unseen zero-day threats without contamination leakage.

Supervised Threat Classification: Train a 300-tree Random Forest classifier on labeled attack patterns, including brute-force login floods, stealthy crypto-miners, covert data exfiltration, and mass file modification.

The 3×3 Combined Risk Engine: Synthesize continuous anomaly scores and attack probabilities through a deterministic lookup matrix that assigns LOW, MEDIUM, HIGH, or CRITICAL severity tiers alongside plain-English explanations for SOC analysts.

Asynchronous FastAPI Service & Live Streamlit SOC Dashboard: Expose real-time predictions via /predict and /health REST API endpoints, visualize live KPIs and severity distributions in Streamlit, and stream telemetry at 23 milliseconds per event.

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