Institutional review previewGraduate program information remains subject to final approval.
info@thefcoe.comContact FCoE
Course catalog

Cybersecurity Management · Proposed course

Artificial Intelligence & Threat Intelligence in Cybersecurity

Course code
CSM 608
Credit hours
3
Program
MSCM
Delivery
Hybrid/Online

Course description

Course overview

This course examines the integration of artificial intelligence (AI) and machine learning (ML) techniques within cybersecurity threat detection, prevention, and intelligence operations. Students will explore AI-based security analytics, anomaly detection, natural language processing for threat analysis, and automation of security response workflows. Through hands-on labs, students will develop and evaluate models for predictive threat intelligence, adversarial AI, and automated security orchestration.

Course content is presented for institutional review and remains subject to approval and revision.

Academic focus

Course objectives

  1. 01

    Understand the application of AI and ML technologies in cybersecurity threat detection and intelligence.

  2. 02

    Develop predictive analytics models for identifying and mitigating cyber threats.

  3. 03

    Apply AI-driven tools to automate security monitoring and incident response.

  4. 04

    Assess ethical considerations and limitations of AI in cybersecurity decision-making.

Learning outcomes

Upon completion

  1. CLO 1

    Apply AI and ML algorithms to analyze and predict cybersecurity threats (maps to PLO 3, PLO 4, PLO 6).

  2. CLO 2

    Utilize data analytics and automation platforms for real-time threat intelligence (maps to PLO 2, PLO 4).

  3. CLO 3

    Evaluate adversarial AI threats and implement countermeasures (maps to PLO 3, PLO 6).

  4. CLO 4

    Demonstrate ethical and responsible use of AI in cybersecurity operations (maps to PLO 7).

Proposed syllabus

Weekly course schedule

The sequence below reflects the current 15-week syllabus and remains subject to institutional review.

  1. Week 1

    Introduction to AI and Threat Intelligence

  2. Week 2

    Data-Driven Cybersecurity and Machine Learning Fundamentals

  3. Week 3

    Supervised and Unsupervised Learning for Threat Detection

  4. Week 4

    Neural Networks and Deep Learning Applications in Cyber Defense

  5. Week 5

    Feature Engineering and Data Preprocessing for Security Datasets

  6. Week 6

    Natural Language Processing (NLP) for Threat and Malware Analysis

  7. Week 7

    Automation in Security Operations (SOAR Platforms)

  8. Week 8

    Midterm Exam and AI Lab Project Proposal

  9. Week 9

    Adversarial AI and Evasion Techniques

  10. Week 10

    AI-Based Intrusion Detection and Behavior Analytics

  11. Week 11

    Predictive Threat Modeling and Intelligence Sharing

  12. Week 12

    Ethics, Bias, and Transparency in AI Cybersecurity

  13. Week 13

    Emerging AI Technologies and Quantum Threats

  14. Week 14

    Final Project Presentations

  15. Week 15

    Final Exam and Course Wrap-Up

Evaluation

Assessment and grading

  • AI Lab Assignments – 25%
  • Midterm Exam – 20%
  • Final Exam – 20%
  • Applied AI Project – 25%
  • Participation and Discussion – 10%
A = 90–100%B = 80–89%C = 70–79%F = Below 70%

Course resources

Required texts and materials

  • Sharma, S., & Kaul, A. (2023). Artificial intelligence for cybersecurity: Techniques and applications. CRC Press.
  • Reddy, K., & Aggarwal, S. (2022). Machine learning and data mining for cybersecurity. Springer.
  • Supplemental datasets, Python notebooks, and AI lab scripts will be provided via the LMS.