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Cybersecurity Analytics Curriculum

Graduate students will learn how to:

  • Apply predictive and probabilistic approaches to assess cyber risk
  • Develop data-driven solutions to real world problems that integrate cybersecurity concepts
  • Identify critical cybersecurity issues across industries
  • Analyze and evaluate systems to maintain operations in the presence of risks and threats

Program Coursework

Required Courses

The following eight courses, worth 24 credits, are required:

DATA 6000 - Applied Statistics
DATA 6150 - Data Science Foundations
DATA 6200 - Data Management
DATA 6250 - Machine Learning for Data Science
COMP 5500 - Advanced Network Security
COMP 6500 - Threat Intelligence
COMP 7500 - Thesis I and COMP 7550 - Thesis II OR
DATA 6500 - Capstone I and DATA 6550 - Capstone II

Elective Courses

The remaining credits are electives from two different categories. Software Security Elective, which will require students to either securely develop software and applications, or sufficiently deconstruct existing software, applications, or malware to identify and evaluate its operation. Cybersecurity Analytics electives, which seek to provide a deeper knowledge and comprehension of specific cybersecurity areas, such as cloud computing, IoT, wireless networks, intrusion prevention systems, and more.

Cybersecurity Analytics Elective

COMP 5530 - IoT Security
COMP 5560 -Wireless Networks & Security
COMP 5570 - Cloud Computing, Security, & Forensics
COMP 6555 - Intrusion Detection & Prevention
COMP 6580 - Digital Forensics & Incident Response
Second Course from Software Security
Approved security-focused course(s) from other disciplines or schools

Software Security Elective

COMP 5520 - Malware Analytics
COMP 5420 - Reverse Engineering
COMP 6520 - Secure Software Design & Development