About The Program

The Master of Science in Cybersecurity and Information Assurance (CIA) is a 30-credit hour graduate degree offered by the Department of Computer and Information Science (CIS). This initiative reflects the University’s eagerness to address rising needs of cybersecurity professionals in both the private and public sectors. The CIA program educates and trains an elite, diverse group of students who want to pursue a career in cybersecurity, such as cybersecurity analysts/specialists, cybersecurity engineers, network engineers/architects, software developers, etc. The program will also benefit anyone on this campus who is interested in advancing their knowledge of computer security and privacy, and it will offer a great opportunity for interdisciplinary inquiry and teaching.

 

If you have additional questions, please contact the program committee chair: dmadma@umich.edu

Program Details

  • Eligibility Requirements

    Regular admission to the program requires a bachelor’s degree in a Science, Technology, Engineering, or Mathematics (STEM) field earned from an accredited program with an average of B (or better). An entering student should have completed one course in probability and statistics, one course in programming, and one course in calculus II (see the table shown below). A course in calculus III and a course in linear algebra are recommended, but not required. 

     

    Programming  Mathematics Statistics (one of these or equivalent required)
    CIS 200/CIS 2001 (or equivalent) required MATH 116 (or equivalent required)

    IMSE 317

    CIS 350 (or equivalent) recommended MATH 215 (or equivalent recommended) STAT 326
      MATH 227 (or equivalent recommended) MATH 425

     

    Deficiencies in the prerequisites may be satisfied after entrance into the program. Students with such deficiencies must complete the missing prerequisite course(s) with a grade of "B" or better within the first two semesters after entering the program. 

  • Prerequisite Course Descriptions

    Programming

    CIS 200 Computer Science II

    • Prerequisites: MATH 115 and (CIS 150 or IMSE 150 or CCM 150)
    • Description: This course presents techniques for the design, writing, testing, and debugging of medium-sized programs, and an introduction to data structures (stacks, queues, linked tests) using an object-oriented programming language. Topics covered include pointers, templates, and inheritance. The principles of UML modeling are continued. This course will consist of three lecture hours and one two-hour laboratory. (4 credits). 

    CIS 2001 Computer Science II for Data Scientists

    • Prerequisites: CIS 1501 and MATH 115
    • Description: This course presents techniques for the design, writing, testing, and debugging of medium-sized programs, and an introduction to data structures (stacks, queues, linked tests) using an object-oriented programming language for data science applications. Topics covered include pointers, templates, and inheritance. The principles of UML modeling are continued. This course will consist of three lecture hours and one two-hour laboratory. The labs will cover various data science applications. (4 credits). 

    CIS 350 - Data Structures and Algorithm Analysis

    • Prerequisite: MATH 115 and (CIS 200 or IMSE 200) and CIS 275
    • Description: A focus on data and algorithm design. Data design topics include object-oriented discussions of hashing, advanced tree structures, graphs, and sets. Algorithm design topics include the greedy, divide-and- conquer, dynamic programming, backtracking, and branch-and-bound techniques. A significant discussion of algorithm complexity theory, including time and space trade-offs and elementary computability theory, is included. (4 credits ).

    Mathematics

    MATH 116 Calculus II

    • Prerequisites: MATH 115
    • Description: Transcendental functions, techniques of integration, improper integral, infinite sequences and series, Taylor's theorem, topics in analytic geometry, polar coordinates, and parametric equations. This course includes computer labs. (4 credits).

    MATH 215 Calculus III (Recommended)

    • Prerequisites: MATH 116
    • Description: Vectors in the plane and space, vector-valued functions and curves, functions of several variables including limits, continuity, partial differentiation and the chain rule, multiple integrals and coordinate transformations, integration in vector fields, and Green's and Stokes' theorems. This course includes computer labs. (4 credits). 

    MATH 227 Introduction to Linear Algebra (Recommended)

    • Prerequisites: MATH 116
    • Description: An introduction to the theory and methods of linear algebra with matrices. Topics include: systems of linear equations, algebra of matrices, matrix factorizations, vector spaces, linear transformations, eigenvalues and eigenvectors, science and engineering applications, and computational methods. (3 credits).

    Statistics

    IMSE 317 Engineering Probability and Statistics

    • Prerequisites: MATH 116 or MATH 114
    • Description: Set theory, combinatorial analysis, probability and axioms, random variables, continuous and discrete distribution functions, expectations, Chebychev's inequality, weak law of large numbers, central limit theorem, sampling statistics and distributions, point and interval estimation and linear regression. (3 credits). 

    STAT 326 Applied Statistics II

    • Prerequisites: STAT 325
    • Description: A continuation of STAT 325. This course treats both the principles and applications of statistics. Elementary theory of estimation and hypothesis testing, the use of the normal, chi- square, F and t distributions in statistics problems will be covered. Other topics selected from regression and correlation, the design of experiments, analysis of variance, analysis of categorized data, nonparametric inference, and sample surveys. (3 credits). 

    MATH 425 Mathematical Statistics

    • Prerequisites: MATH 325
    • Description: Interval estimation and pivotal quantities, maximum likelihood estimation, hypothesis tests, linear models and analysis of variance, bivariate normal distribution, regression and correlation analysis, and nonparametric methods. (3 credits). 
  • Curriculum

    To satisfy the requirements for the MS degree in Cybersecurity and Information Assurance, all students admitted to the program are expected to complete a minimum of 30 credit hours of graduate coursework, with a cumulative grade point average of B or better. The program of study consists of core courses, concentration courses and electives with coursework/project/thesis options.

    Option 1: MS Coursework. This option requires a minimum of 30 credits be earned through coursework. The minimum requirements are as follows:

    • Core courses - 9 credit hours
    • Concentration courses -12 credit hours
    • Elective courses - 9 credit hours

    Option 2: MS Project. This option requires a project report describing the results of an independent study project under the supervision of the advisor. The scope of the research topic for the project should be defined in such a way that a full-time student could complete the requirements for a master’s degree in twelve months or three semesters following the completion of course work by regularly scheduling graduate research credits. The minimum requirements are as follows:

    • Core courses - 9 credit hours
    • Concentration courses -12 credit hours
    • Elective courses - 6 credit hours
    • Master’s Project - 3 credit hours

    Option 3: MS Thesis. This option requires a research thesis prepared under the supervision of the advisor. The thesis describes a research investigation and its results. The scope of the research topic for the thesis should be defined in such a way that a full-time student could complete the requirements for a master’s degree in twelve months or three semesters following the completion of course work by regularly scheduling graduate research credits. The minimum requirements are as follows:

    • Core courses - 9 credit hours
    • One concentration area - 12 credit hours
    • Elective courses - 3 credit hours
    • Master’s Thesis - 6 credit hours
  • Core Courses

    Core Courses............................................ 9 credit hours

    All Students are required to take the following courses:

    Required Core courses (9 credits)

    • CIS 540           Foundation of Information Security (3)
    • IMSE 570/CIS 564      Enterprise Information Systems (3)
    • ACC 601         Information Tech Auditing: IS Audit and Security (3)

     

  • Concentration Areas

    Concentration Courses.......................... 12 credit hours from one of the three areas listed below.

     Area 1: Network & System Security

    Concentration Area 1 focuses on security and privacy issues in various computer systems and networks including wireless networks and mobile devices.

    • CIS 544     Computer and Network Security (3)

    • CIS 546     Wireless and Mobile Security (3)

    • CIS 584     Advanced Computer and Network Security (3)

    Choose exactly one from the following:

    • CIS 527 Computer Networks (3)

    • CIS 535 Programmable Mobile/Wireless Technologies and Pervasive Computing (3)

    • ECE 5541 Embedded Networks (3)

     Area 2: Data & Application Security

    Concentration Area 2 focuses on security and privacy of data and applications including multimedia data, cloud data, and “big data.”

    • CIS 545       Data Security and Privacy (3)
    • CIS 548       Security and Privacy in Cloud Computing (3)
    • ECE 527     Multimedia Security and Forensics (3)

    Choose exactly one from the following:

    • CIS 5570    Introduction to Big Data (3)
    • HHS 570    Information Science and Ethics (3)
    • MIS 642     Information Assurance (3)

    Area 3: Software Security

    Concentration Area 3 focuses on software vulnerability and secure software development in various systems.

    • CIS 549       Software Security (3)
    • CIS 553       Software Engineering (3)
    • CIS 565       Software Quality Assurance (3)

    Choose exactly one from the following:

    • CIS 525   Web Technology (3)
    • CIS 579   Artificial Intelligence (3)
    • CIS 580   Data Analytics for Software Engineering (3)
  • Electives and Options

    Electives and Options (9 credits)

    The following areas are provided for guidance only. Students are allowed to select elective courses from the same or different areas.

    • Option 1: MS Coursework. Choose 9 credit hours (or 3 courses) from the following list below
    •  Option 2: MS Project. Choose 6 credit hours (or 2 courses) from the following list below and CIS 695 Master's Project for 3 credits.
    •  Option 3: MS Thesis. Choose 3 credit hours (or 1 course) from the following list below and CIS 699 Master’s Thesis for 6 credits.

     Note: An elective course should not be the same as any course taken to satisfy concentration course requirements.

    Area 1: Security, Privacy, Forensics, and Auditing

    •  ACC 505 - Development and Interpretation of Financial Information
    •  CIS 544 - Computer and Network Security
    • CIS 545 - Security and Privacy in Cloud Computing
    • CIS 546 - Wireless and Mobile Security
    •  CIS 548 - Cloud Computing Security
    • CIS 549 - Software Security
    •  CIS 584 - Advanced Computer and Network Security
    •  ECE 527 - Multimedia Security and Forensics
    •  HHS 570 - Information Science and Ethics
    • MIS 642 - Information Assurance

    Area 2: Systems, Networks and Communications

    •  CIS 527 - Computer Networks
    • CIS 537 - Advanced Networking and Distributed Systems
    • CIS 574 - Compiler Design
    • CIS 578 - Advanced Operating Systems
    • CIS 647 - Research Advances in Networking and Distributed Systems
    • ECE 526 - Multimedia Communication Systems
    •  ECE 535 - Mobile de & Ubiquitous Computer Systems
    •  ECE 550 - Communication Systems
    •  ECE 5541 - Embedded Networks
    •  ECE 570 - Computer Networks
    •  ECE 5701 - Wireless Communications
    • ECE 5702 - High-Speed and Advanced Networks
    •  ECE 531 - Intelligent Vehicle Systems
    •  ISM 525 – Computer and Information Systems

    Area 3: Data Management, Analytics, and Intelligent Systems

    • CIS 536 - Information Retrieval 
    • CIS 556 - Database Systems
    •  CIS 5570 - Introduction to Big Data
    • CIS 562 - Web Information Management
    •  CIS 568 - Data Mining
    • CIS 5700 - Advanced Data Mining
    •  CIS 579 - Artificial Intelligence
    • CIS 585 - Advanced Artificial Intelligence
    •  CIS 586 - Advanced Data Management Systems
    •  CIS 658 - Research Advances in Data Management Systems
    •  ECE 531 - Intelligent Vehicle Systems
    •  ECE 537 - Data Mining
    •  ECE 552 - Fuzzy Systems
    •  ECE 579 - Intelligent Systems
    • ECE 5831 - Patter Recognition & Neural Networks 

    Area 4: Software Engineering 

    • CIS 505 - Algorithm Design and Analysis
    •  CIS 525 - Web Technology
    •  CIS 535 - Programmable Mobile/Wireless Technologies and Pervasive Computing
    • CIS 550 - Object-Oriented Programming
    •  CIS 553 - Software Engineering
    •  CIS 565 - Software Quality Assurance
    •  CIS 566 - Software Architecture and Design Patterns
    •  CIS 571 - Web Services
    • CIS 575 - Software Engineering Management
    •  CIS 577 - Software User Interface Design
    •  CIS 580 - Data Analytics in Software Engineering
    •  CIS 678 - Advances in Software Engineering Research
    •  CIS 577 - S/W User Interface Design and Analysis

    Area 5: Human Computer Interface Design

    •  IMSE 514 - Multivariate Statistics
    •  IMSE 559 - System Simulation
    •  IMSE 577 - Human-Computer Interaction for UI/UX Design
    •  IMSE 586 - Big Data Analysis and Visualization
    •  HCDE 530 - Information Visualization
    •  HCDE 540 - Industrial Design
    •  HCDE 510 - Foundation of HCDE
    •  HCDE 501 - Human Factors and Ergonomics

Computer and Information Science

105
CIS Building
Phone: 
313-436-9145
Fax: 
313-593-4256
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