Overview What You Will Learn Curriculum Instructor

Course Overview

Threat hunting is a proactive cybersecurity practice that helps organizations identify hidden threats that may evade traditional detection mechanisms. By combining threat intelligence, telemetry analysis, data science, machine learning, and SIEM technologies, security teams can uncover suspicious behaviors and improve their ability to detect advanced threats. This course begins with the foundations of industrial threat hunting, including hunt planning, documentation, methodologies, telemetry sources, essential tools, and MITRE ATT&CK-based hunt matrices. It then covers log parsing, data cleaning, feature engineering, security-focused visualization, and Python-based analysis using Jupyter. Learners explore unsupervised machine learning techniques such as Isolation Forest, DBSCAN, K-Means, entropy-based methods, and Z-score detection for anomaly identification. The course concludes with operational threat hunting using Splunk and Jupyter, including SPL-based data preparation, detection pipelines, SIEM integration, and practical hunt execution. You will learn to plan and execute structured threat hunts, transform security telemetry into actionable insights, apply machine learning for anomaly detection, and operationalize hunting workflows using Splunk and Jupyter.

What You Will Learn

  • Explore the threat hunting lifecycle and how ML augments hypothesis-driven investigation.
  • Analyze raw log data by cleaning, enriching, and visualizing it using Pandas, Seaborn, and Matplotlib in Jupyter.
  • Apply anomaly detection techniques such as Isolation Forest and DBSCAN on telemetry data.
  • Design and execute a complete ML-based hunt in Splunk and Jupyter to detect suspicious behavior.

Program Curriculum

  • Course Introduction
  • Module Introduction
  • Overview of Threat Hunting Concepts and Importance
  • How to Plan Threat Hunt
  • How to Document Threat Hunt
  • Hunting Methodologies
  • Telemetry and Data Sources
  • Essential Tools for Threat Hunting
  • Explore MITRE ATT&CK
  • How to Use MITRE Navigator
  • From ATT&CK to Action: Building a Hunt Matrix for Real Threats
  • Chapter 1 Quiz

  • Module Introduction
  • Parsing and Cleaning Logs
  • Techniques for Log Parsing and Cleaning - Part 1
  • Techniques for Log Parsing and Cleaning - Part 2
  • Introduction to Feature Engineering
  • Visualizing Behaviors
  • Threat Hunting Visualization
  • What Is Security-focused Visualization
  • Create Your Own Visualization
  • Top Security Visualizations Every Threat Hunter Should Use
  • Chapter 2 Quiz

  • Module Introduction
  • Introduction to Unsupervised ML
  • Understand Different Processes of Unsupervised Models
  • Evaluating and Tuning ML Models
  • Suspicious Login Hunting
  • Graphical Representation of Anomaly
  • Event Correlation
  • General Pitfalls in Threat Detection
  • Different ML Techniques
  • How to Choose Best ML Model
  • Chapter 3 Quiz

  • Module Introduction
  • Understand Splunk Architecture
  • Log Ingestion in Splunk
  • Search Operation in Splunk
  • Writing SPL for Data Prep
  • Jupyter Detection Pipeline
  • Threat Hunt Notebook Example
  • Splunk and Jupyter Integration - Part 1
  • Elastic and Jupyter Integration - Part 2
  • Real Hunt Execution
  • Course Wrap-up Video
  • Chapter 4 Quiz
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Instructor

Team

Starweaver delivers 10x better-trained employees and students through scalable, activity-based online learning combined with live human-to-human instruction. With 70–85% course completion rates, we go beyond passive content libraries by focusing on real skill-building and professional competency. Our mission is to transform technologists into world-class experts and business professionals into tech-savvy leaders. Starweaver connects learners with a global network of live instructors and peers, driving higher engagement, satisfaction, and achievement. Our proprietary tools blend guided self-learning with real-time collaboration, ensuring learners stay motivated, capable, and truly job-ready.

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