Cybersecurity

Master of Science

The objective of our MIS 516 course is to provide students a thorough and operational knowledge of information security so that this critical area is recognized as a management issue and not an IT issue.

The information security arena contains a broad array of multi-level models for assessing, planning, implementing and monitoring the mitigation of security risks. At the very core of this information security spectrum are the actual system and network devices which store, manage, transmit and secure information. This course is designed to provide a working knowledge of issues and techniques surrounding the proper safeguarding of operating systems and related components. Filled with Information Assurance topics, this course offers a solid base for system administrators and technical managers.

Corporations today are said to be data rich but information poor. For example, retailers can easily process and capture millions of transactions every day. In addition, the widespread proliferation of economic activity on the Internet leaves behind a rich trail of micro-level data on consumers, their purchases, retailers and their offerings, auction bidding, music sharing, so on and so forth. Data mining techniques can help companies discover knowledge and acquire business intelligence from these massive datasets. This course will cover data mining for business intelligence. Data mining refers to extracting or “mining” knowledge from large amounts of data. It consists of several techniques that aim at discovering rich and interesting patterns that can bring value or “business intelligence” to organizations. Examples of such patterns include fraud detection, consumer behavior, and credit approval. The course will cover the most important data mining techniques -- classification, clustering, association rule mining, visualization, prediction.

This course is designed to provide students with a hands-on introduction to the fundamental concepts and tools of modern cyber threat intelligence. Students will become familiar with the cyber threat intelligence lifecycle, identifying, collecting, and integrating intelligence feeds, common intelligence formats, and standard cyber threat intelligence technologies (e.g., CIF servers, TAXII servers, SIEM's etc.).

This course introduces students to the principles and techniques of the cybersecurity practice known as penetration testing (pen testing), or ethical hacking, and covers the full pen test life cycle. Students discover how system vulnerabilities can be exploited and learn how to avoid such problems. Students will review various tools and methods commonly used to compromise information and control systems. Ethical hacking, also known as penetration testing, is the act of breaking into a system with the permission and legal consent of the organization or individual who owns and operates the system, with the purpose of identifying vulnerabilities to strengthening the organization's security. Students will conduct hands-on penetration tests in a lab environment to practice the concepts presented and tools reviewed in the course. This course is an ethical hacking course and students will learn hacking techniques within a controlled environment for the goal of better securing the IT resources of their rightful owners.

The focus of this course is the usage of common tools used during penetration assessments and hardening system defenses. Students will draw from previous classes to combine skills in online defense and penetration exercises of systems in a virtual environment. Along with course labs, this course will apply theory and techniques to provide the following learning base - knowledge, comprehension, and application.

This course provides an introduction to technical aspects of cyber security. It describes threats and types of attacks against computers and networks to enable students to understand and analyze security requirements and define security policies. Security mechanisms and enforcement issues will be introduced. Students will be immersed in the cyber-security discipline through a combination of intense coursework, open-ended and real-world problems, and hands-on experiments.

Machine learning deals with the automated classification, identification, and/or characterizations of an unknown system and its parameters. There are an overwhelming number of application-driven fields that can benefit from machine learning techniques. This course will introduce you to machine learning and develop core principles that allow you to determine which algorithm to use, or design a novel approach to solving to engineering task at hand. This course will also use software technology to supplement the theory learned in the class with applications using real-world data.

Cloud computing is the model for ubiquitous, convenient, on-demand access to a shared pool of configurable computing resources. With the interest in cloud computing, the security challenges are raising concerns. This class discusses the cloud computing architecture and components along with the threat modelling and discusses physical, database, network, virtualization, services, and users level security concerns and their solutions.

Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions.

The purpose of the course is to give students a comprehensive introduction to digital communication principles. The major part of the course is devoted to studying how to translate information into a digital signal to be transmitted, and how to retrieve the information back from the received signal in the presence of noise and intersymbol interference (ISI). Various digital modulation schemes are discussed through the concept of signal space. Analytical and simulation models for digital modulation systems are designed and implemented in the presence of noise and ISI. Optimal receiver models for digital base-band and band-pass modulation schemes are covered in detail.

Process and tools for systems engineering of large-scale, complex systems: requirements, performance measures, concept exploration, multi-criteria tradeoff studies, life cycle models, system modeling, etc.