Note that students in this program are required to take three technical electives: Machine Learning and Security, Introduction to Cryptography, and Biometrics. Students may choose from the additional elective courses listed below to fulfill the other 6 elective credit requirements.
Learn more about each course in the following descriptions.
This course exposes students to the technical and behavioral reasons why computers and computer networks increasingly need to have adequate security measures that can safeguard sensitive information. Students begin by investigating the security measures that can be employed to safeguard information with a focus on the theory that goes into designing these measures, as well as studying common and proven security tools and techniques.
This course provides an in-depth study of various network attack techniques and methods to defend against them. A number of threats and vulnerabilities of the Internet will be covered, including various vulnerabilities of TCP/IP protocols, denial of service (DOS), attacks on routing, attacks on DNS servers, and TCP session hijacking. This course will also cover defending mechanisms, including intrusion detection, firewalls, tracing the source of attacks, anonymous communication, IPsec, virtual private network, and PKI. To make it easy for students to understand these attacks, basics of the TCP/IP protocols will also be covered in the course. The course adopts the "learning by doing" principle. Students will learn the attacks by performing them in a restricted environment or a simulated environment. They will also play with a number of security tools to understand how they work and what security guarantee they provide. The experiments will be conducted in the virtual machine environments.
Areas of Study
Internet architecture; security and attacks on TCP/IP, DNS, and BGP protocols; Internet protocol security; firewalls; intrusion detection; network traceback; web security; encryption; Public Key infrastructure; one-way harsh function; digital signature; and security protocols.
In this course, students will explore the foundational theories, concepts, and computer-assisted reasoning tools necessary for creating assured systems.
Areas of Study
Functional programming; theorem proving; and logic for reasoning about access control, security, and trust.
Design and Analysis of Algorithms – 3 Credits
This course covers topics related to algorithm design and analysis. Students will develop the skills to divide and conquer algorithms, greedy algorithms, graph algorithms, algorithms for social networks, computational biology, optimization algorithms, randomization, and algorithm analysis. Assignments and project work will be broad in scope but will emphasize algorithmic thinking, performance guarantees and boundary cases, efficient solutions to practical problems and understanding how to analyze algorithms. More advanced topics will have students work with modern algorithms for real-world applications.
Areas of Study
Asymptotic analysis and recurrences; classical numeric algorithms; advanced data structures; graph algorithms; divide-and-conquer, greedy choice, dynamic programming, and other computational strategies; and NP-completeness.
Students will review classical operating system concepts (process and memory management, process coordination, device drivers, file systems, starvation/deadlock) before moving into modern topics of files systems (such as log-structured file systems, distributed file systems, memory-based file systems). Assignments and lab work is focused on operating system design (monolithic, communication-kernel, extensible/adaptable, distributed shared memory), multiprocessor issues (scheduling, synchronization, IPC), and some aspects of security (internet attacks, encryption, defenses). Students will also gain experience with Inspection and modification of actual operating system code (Linux).
The course includes weekly lab using a Unix-like operating system.
Areas of Study
Design and implementation of operating systems; process and memory management; resource scheduling; file system management; I/O and kernel services; and structuring.
Fundamentals of Data and Knowledge Mining – 3 Credits
This course will introduce popular data mining and statistical methods for extracting knowledge from data. The principles and theories of data mining methods will be discussed and will be related to the issues in applying data mining to real world problems. Students will also acquire hands-on experience using R programming language and Weka software to develop data mining solutions to scientific, social or business problems. The focus of this course is in understanding data and how to identify the right data mining techniques and formulate data mining tasks in order to solve problems using the data aided by data mining techniques.
Students will explore advanced concepts and state-of-the-art developments in computer architecture: memory systems, pipelining, simultaneous multithreading, run-time optimization, array processing, parallel processing, multiprocessing, abstract analytic models, power-aware computing, embedded computing, relationship between computer design and application requirements, cost/performance tradeoffs, and many example computers of interesting and unusual features. This course work and student projects will cover the principles, characteristics, and trends of computer systems design at a level appropriate for all computer scientists and computer engineers. The course focuses largely on hardware design, to include aspects of the complete system, comprising the hardware, operating system, compilers, and application software. Students will also gain an understanding of the hardware technology that has fueled the rapid progress of computer systems and complete case studies of current systems.
Areas of Study
Advanced computer architecture including discussion of instruction set design (RISC and CISC); virtual memory system design; memory hierarchies; cache memories; pipelining; vector processing; I/O subsystems; co-processors; and multiprocessor architectures.
Machine Learning – 3 Credits
This course explores the intersection of machine learning and computer security and offers an in-depth introduction to machine learning theory and methods. Students explore research problems in machine learning and its applications in security contexts. Topics include inductive learning, neural network approaches, computational learning theory, data mining, fraud detection, pattern recognition, and other contemporary applications.
This course provides an overview of classical and public-key cryptography. Topics include classical cryptosystems and their cryptanalysis, RSA and other public key cryptosystems, pseudo-random sequences, zero-knowledge protocols, related ethical and social concerns.
Biometrics – 3 Credits
This course introduces biometric technologies and modalities including fingerprint recognition, iris recognition, and facial recognition. Students experiment with several types of biometric authentications including fingerprint scanning, retinal scanning, facial recognition, and voice analysis. Students work with the sciences and technologies used to measure and analyze unique biological traits to safeguard identity, and learn about emerging trends in the industry.
Advanced Data Structures and Algorithms – 3 Credits
In this course, students will explore internals of all major data structures. Topics will include algorithms for sorting, balancing trees, graph querying, hashing and compression. Students will also develop expertise in cache effects and using parallel algorithms.
This course focuses on design and implementation of mobile applications for Android. The primary focus is on UI development. In addition to that, the following topics are also covered: efficient use of persistent storage, network services, GPS, maps and sensors, working with Rest API and developing web services for mobile apps, developing and using cloud based services for mobile apps. Students are expected to have a good understanding of object oriented programming.
Software Engineering – 3 Credits
This course covers a broad spectrum of software engineering topics. Students will work through the complete software engineering process, models in software engineering, requirements and specifications, design techniques, functional decomposition, data flow, data structures, theoretical issues in testing, testing strategies, and cost and reliability models. The course is based entirely on a practical industry perspective, including current industry standards and usages for concepts covered in class. Students will emerge from the course with the knowledge and skills necessary to quickly learn, adapt to, or modify an organization’s specific software development processes.
Areas of Study
Requirements and specifications including tools such as PSL/PSA and SREM; design techniques; functional decomposition; data flow; data structure; theoretical issues in testing and testing strategies; domains; mutation and error; and cost and reliability models.
In this course, students will learn essential project-based skills, including software modeling, architecture, design, and implementation using techniques such as diagramming, analysis tools, and common sense engineering methods. Students will also analyze performance of concurrent, message-driven systems.
Immersions allow students to meet and collaborate with their peers, learn from guest speakers who are industry experts, and participate in collaborative workshops. Immersions are held on campus Friday through Sunday of the first week of the semester starting in Fall 2017. Students will be required to attend at least one immersion that is not offered for credit during the degree program.
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