Lilly Tech Systems
AI-Powered Threat Detection
Master advanced threat detection using AI and machine learning. Learn User and Entity Behavior Analytics (UEBA), ML-driven threat hunting, MITRE ATT&CK mapping, real-time anomaly detection, and intelligent alert prioritization.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
The threat detection landscape, why AI matters, and key concepts in ML-powered security detection.
2. Anomaly Detection
Statistical and ML approaches to anomaly detection, baseline modeling, and reducing false positives.
3. Behavioral Analytics
UEBA platforms, user profiling, peer group analysis, and entity behavior modeling for insider threats.
4. Log Analysis
ML-powered log parsing, event correlation, timeline reconstruction, and threat hunting with AI.
5. Real-time Detection
Stream processing, online learning, MITRE ATT&CK mapping, and real-time alert generation.
6. Best Practices
Detection engineering, model lifecycle management, metrics, and building detection-as-code practices.
What You'll Learn
By the end of this course, you'll be able to:
Detect Anomalies
Build and deploy anomaly detection models that identify threats traditional rules miss.
Analyze Behavior
Implement UEBA to detect insider threats and compromised accounts through behavioral modeling.
Hunt Threats
Use ML-assisted threat hunting techniques to proactively find advanced persistent threats.
Prioritize Alerts
Build intelligent alert prioritization systems that surface the most critical threats first.