Project 18: AI & ML for Threat Detection and SOC Automation
Explore how artificial intelligence and machine learning are transforming threat detection and security operations through hands-on experimentation with anomaly detection models.
Project 18: AI & ML for Threat Detection and SOC Automation
Explore how artificial intelligence and machine learning are transforming threat detection and security operations through hands-on experimentation with anomaly detection models.
AI & ML for Threat Detection and SOC Automation
Overview
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how security teams detect, analyze, and respond to threats. This project introduces students to the fundamentals of AI and ML in cybersecurity, with a specific focus on anomaly detection and automation within Security Operations Centers (SOCs).
What You Will Learn
- The role of AI and ML in modern cybersecurity operations
- How supervised and unsupervised ML techniques apply to threat detection
- Creating and training simple models for anomaly detection in log data
- Evaluating the performance of ML-based threat detection vs rule-based detection
- How AI supports SOC automation, alert prioritization, and triage
Hands-On Learning
Students will gain experience using:
- Python-based ML tools like scikit-learn or PyCaret
- Jupyter Notebooks for model building and experimentation
- Real or synthetic log datasets to detect anomalies
- Comparative analysis between traditional rule-based alerts and ML-powered alerts
- Visualization tools to explain model outcomes and anomalies
đź”— Continue to the hands-on lab for full instructions!
AI & ML for Threat Detection Lab
AI & ML for Threat Detection Lab Overview In this lab, you’ll apply machine learning techniques to detect anomalies in log data—mimicking how AI supports SOC teams. You’ll use Python tools such as scikit-learn or PyCaret to train unsupervised models and compare results to traditional rule-based detection. Lab Instructions 1. Setting Up Your Environment You will need: Python 3.9+ Jupyter Notebook or Google Colab Install required libraries: pip install pandas matplotlib seaborn scikit-learn pycaret Download a sample log dataset (or use the instructor-provided logs). Suggested sources: ...