Syllabus: AINS6300 AI in Threat Detection

Catalog Description

Applies AI to telemetry, anomaly detection, threat intelligence, detection engineering, and SOC integration.

Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

Weekly Schedule

Week	Topic	Essential Question	Deliverable
1	Security telemetry and threat models	What signals reveal malicious behavior?	Lab notebook + assignment brief
2	Anomaly detection foundations	How can models detect unknown patterns?	Lab notebook + assignment brief
3	Malware and network behavior analysis	What features distinguish hostile activity?	Lab notebook + assignment brief
4	Threat intelligence and enrichment	How does external intelligence improve detection?	Lab notebook + assignment brief
5	Detection engineering and evaluation	How do we measure detection quality?	Lab notebook + assignment brief
6	Adversarial behavior and evasion	How do attackers adapt to detectors?	Lab notebook + assignment brief
7	Security operations integration	How do detections become action?	Lab notebook + assignment brief
8	Threat detection portfolio	What evidence supports deployment?	Lab notebook + assignment brief

Assessment

Component	Weight
Weekly labs and notebooks	30%
Applied assignments	35%
Participation and technical critique	15%
Final synthesis portfolio	20%

Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.