Master of Science in Cybersecurity with a Concentration in Data Science Online

Lecture/theory course considers the current methods, practices, and standards used to enable communication on computer and voice networks. This includes a study of the physical layers, architectural layers, design, operation, management, and ISO standards, with particular and telephony technologies. Both local and wide area networks are examined.

This course introduces students to the differences between cybercrime, cyber espionage, and cyber warfare by discussing the relationship of cyber intrusions and cybersecurity to nations, businesses, society, and people. Students will use case studies to analyze the threats, vulnerabilities and risks present in these environments, and develop strategies to reduce the breaches and mitigate the damages.

Computers have made organizations easier to run. All accounting information, inventory records, customer data, and intellectual property that an organization possesses is contained somewhere in an electronic file. As such, these electronic files are vulnerable to attacks from both employees and outsiders from around the world. This course will provide the student with an understanding of how computer fraud and manipulation is accomplished and what security measures should be instituted to prevent it.

This course examines techniques used to conduct computer crime investigations and gather probative evidence to secure a conviction under state and federal laws. Students will simulate a computer forensic investigation: developing an investigation plan, securing the crime scene, analyzing evidence, preparing the case for court, and testifying in a moot court situation.

Students will study and implement basic computer and network security strategies on Window and Linux networks. Students examine and analyze network traffic, including investigating wireless transmission, install firewalls and define Internet Protocol Security Controls (IPSEC). Labs include system hardening, dissecting network packet structure and creating encryption formats; managing authentication and access controls. Students study implementing a public key infrastructure and best strategies for using intrusion detection systems.

The capstone project is an opportunity to pursue an independent learning experience focused on a specific aspect of economic crime forensics based on the student's interest. The capstone is intended to extend students beyond the coursework and cases to apply knowledge in ways that are relevant to their professional goals. Students will work on a research project or in an experiential learning environment. Each student will be required to present his/her capstone both as an oral presentation and a summary written document.

This course introduces students to mathematical models that can be employed to make informed decisions in a wide variety of data-driven fields, including (but not limited to) finance, banking, marketing, health care, retail, manufacturing, and transportation. Goals such as increasing revenue, decreasing costs, and improving overall efficiency of operations in the face of various constraints are considered. Students learn to recognize when a problem lends itself to a particular type of model, formulate the model, and use appropriate methods to solve or extract information from the model. Particular emphasis is placed on linear programming (with exposure to network models and integer programs) and the simplex method. Forecasting, inventory management, and queueing models, as well as Markov chains, are also studied. Additional topics covered include sensitivity analysis, duality, decision analysis, and dynamic programming. Software (both spreadsheets and a computer algebra system) is employed consistently throughout the course to expedite the solution and analysis process; emphasis will be placed on the practical application of models rather than on the models' mathematical properties.

This course entails analysis and evaluation of database designs in relation to the strategic mission of the project. Topics include database systems, database architectures, and data-definition and data-manipulation languages. Also included are logical and physical database design, database models (e.g., entity-relationship, relational), normalization, integrity, query languages including SQL, and relational algebra, in addition to social and ethical considerations and privacy of data. This course incorporates case studies and a project using a relational DBMS.

This course introduces the field of data mining, with specific emphasis on its use for Machine Learning algorithms. Techniques covered may include conceptual clustering, learning decision rules and decision trees, case-based reasoning, Bayesian analysis, genetic algorithms, and neural networks. The course covers data preparation and analysis of results. Skills in Microsoft Excel are useful.

This course introduces students to the field of artificial intelligence (AI). Students will learn how big data and data mining techniques are utilized by machines to create the AI models used by autonomous aircraft and automobiles, personal assistants, IT security software, fraud investigations and credit bureaus. The course will review the history, present day use, and future of artificial intelligence. Through case studies and current events, students will examine the benefits and risks associated with AI. The course will cover issues related to AI and privacy, ethics, and machine bias. Neuromorphic computing, the Open Neural Network Exchange (ONNX), and data analytics will also be discussed.

This course will require students to learn the R programming language and assess how to use it and find interesting features in data. Students will learn about R and statistical best practices and how to display data in a manner that will help you explain your findings to those who do not have a technical background. Moreover, the course introduces students to modeling and simulation. Topics may include basic queueing theory, the role of random numbers in simulations, and the identification of input probability distributions.