AINS6300: AI in Threat Detection#
Aurnova MSAI track: Cybersecurity AI
Credits: 3
Format: 8-week online graduate course
Applies AI to telemetry, anomaly detection, threat intelligence, detection engineering, and SOC integration.
This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.
Course Outcomes#
By the end of the course, students will be able to:
explain the major concepts and tradeoffs in AI in Threat Detection;
build or evaluate applied AI artifacts aligned with the course domain;
document assumptions, evidence, limitations, and operational risks;
connect technical work to governance, stakeholder needs, and deployment readiness.
Module Map#
Security telemetry and threat models — What signals reveal malicious behavior?
Anomaly detection foundations — How can models detect unknown patterns?
Malware and network behavior analysis — What features distinguish hostile activity?
Threat intelligence and enrichment — How does external intelligence improve detection?
Detection engineering and evaluation — How do we measure detection quality?
Adversarial behavior and evasion — How do attackers adapt to detectors?
Security operations integration — How do detections become action?
Threat detection portfolio — What evidence supports deployment?