Program Overview

Cybersecurity Analytics Master of Science

The Master of Science in Cybersecurity Analytics program is designed to enable students to become cybersecurity professionals proficient in data science tools and skills to expedite the response to cybersecurity events. This program blends the disciplines of Cybersecurity and Data Science to give students deeper insight and analysis capabilities for modern enterprise-scale cybersecurity environments.

Program Overview

Cybersecurity Analytics is built on the collection of internal and external data such as logs, scans, and threat intelligence to identify anomalous events. The goal of the program is to provide students with the skills to apply data science tools to modern enterprise cybersecurity platforms.

Program Educational Outcomes

The program educational outcomes for the Master of Science in Cybersecurity Analytics are as follows:

• Apply predictive and probabilistic approaches to assess cyberrisk
• Design data-driven solutions that integrate cybersecurity concepts from the design phase through implementation
• Identify critical cybersecurity issues across different domains or industries
• Analyze and evaluate systems with respect to maintaining operations in the presence of risks and threats

Student Outcomes

Graduate students will be able to demonstrate their mastery of the following skills through the coursework required in the programs:

• Core Knowledge: advanced knowledge in a specialized area consistent with the focus of their graduate program, including critical thinking and problem-solving
• Scholarly Communication: advanced proficiency in written and oral communication, appropriate to purpose and audience
• Professionalism: advanced intellectual and organizational skills of professional practice, including ethical conduct
• Research Methods and Analysis: quantitative and qualitative skills in the use of data gathering methods and analytical techniques used in typical research that is consistent with the focus of their graduate program

Program Requirements

The program has a thesis option with 33 required credit hours, and a non-thesis option with 36 required credit hours. Either option has the students undertake an individualized development experience, either as a two-course Thesis, or a two-course capstone project. Students must complete the course requirements with a cumulative GPA of at least 3.0.

Thesis Option

The course list for the thesis option is as follows:

• SEMESTER 1:
  • DATA6000: APPLIED STATISTICS FOR RESEARCH (3 credits)
  • DATA6150: DATA SCIENCE FOUNDATIONS (3 credits)
  • COMP6500: ADVANCED NETWORK SECURITY (3 credits)
• SEMESTER 2:
  • DATA6200: DATA MANAGEMENT (3 credits)
  • DATA6250: MACHINE LEARNING FOR DATA SCIENCE (3 credits)
  • COMP6550: THREAT INTELLIGENCE (3 credits)
• SEMESTER 3:
  • COMP7500: THESIS I (3 credits)
  • *ELECTIVE (3 credits)
  • *ELECTIVE (3 credits)
• SEMESTER 4:
  • COMP7550: THESIS II (3 credits)
  • *ELECTIVE (3 credits) Total Credits: 33

Non-Thesis Option

The course list for the non-thesis option is as follows:

• SEMESTER 1:
  • DATA6000: APPLIED STATISTICS FOR RESEARCH (3 credits)
  • DATA6150: DATA SCIENCE FOUNDATIONS (3 credits)
  • COMP6500: ADVANCED NETWORK SECURITY (3 credits)
• SEMESTER 2:
  • DATA6200: DATA MANAGEMENT (3 credits)
  • DATA6250: MACHINE LEARNING FOR DATA SCIENCE (3 credits)
  • COMP6550: THREAT INTELLIGENCE (3 credits)
• SEMESTER 3:
  • DATA6900: CAPSTONE I (3 credits)
  • *ELECTIVE (3 credits)
  • *ELECTIVE (3 credits)
• SEMESTER 4:
  • DATA6950: CAPSTONE II (3 credits)
  • *ELECTIVE (3 credits) Total Credits: 36

Electives

A total of 12 semester credit hours of electives must be taken as a part of the program. The available electives are:

• COMP6420: REVERSE ENGINEERING (3 credits)
• COMP6520: MALWARE ANALYSIS (3 credits)
• COMP6580: DIGITAL FORENSICS AND INCIDENT RESPONSE (3 credits) Students may choose, after consultation with their primary advisor, among the electives offered each semester. One of the electives must be either COMP6420 or COMP6520.