This course will introduce many fundamental cybersecurity concepts. The course will teach students to think about information systems using an adversarial mindset, evaluate risk to information systems, and introduce controls that can be implemented to reduce risk. Topics will include authentication systems, data security and encryption, risk management and security regulatory frameworks, networking and system security, application security, organizational and human security considerations, and societal implications of cybersecurity issues. These topics will be discussed at an introductory level with a focus on applied learning through hands-on virtual lab exercises.

Students will gain experience and a better understanding of the field of cybersecurity by gaining practical experience in an area to which cybersecurity is commonly applied, such as software development, networking, or system administration. The goal of this co-op will be to gain a better understanding of the fundamental technologies used in the cybersecurity industry and experience in how professional teams in cybersecurity-adjacent fields operate. Students will be evaluated by their employer. If a transfer student, one term in residence must be completed at RIT carrying a full academic load. (Permission of the Department)

Students will gain experience and a better understanding of the application of technologies discussed in classes by working in the field of computing security. Students will be evaluated by their employer. If a transfer student, they must have completed one term in residence at RIT and be carrying a full academic load.

The course provides students an opportunity to explore methods and applications in cyber analytics with advanced machine learning algorithms including deep learning. Students will learn how to use machine learning methods to solve cybersecurity problems such as network security, anomaly detection, malware analysis, etc. Students will also learn basic concepts and algorithms in machine learning such as clustering, neural networks, adversarial machine learning, etc. Students taking this course should have the 4th year status and completed MATH-190 Discrete Math, MATH-251 Probability and Statistics I, and MATH-241 Linear Algebra.

The course provides students an opportunity to explore methods and applications in cyber analytics with advanced machine learning algorithms including deep learning. Students will learn how to use machine learning methods to solve cybersecurity problems such as network security, anomaly detection, malware analysis, etc. Students will also learn basic concepts and algorithms in machine learning such as clustering, neural networks, adversarial machine learning, etc. A key component of the course will be an independent exploratory project to solve a security program with machine learning algorithms. Students taking this course should have knowledge in Discrete Math, Probability and Statistics, and Linear Algebra. Students should also be able to program in Python.

This course explores current topics in Computing Security. It is intended as a place holder course for faculty to experiment new course offerings in Computing Security undergraduate program. Course specific details change with respect to each specific focal area proposed by faculty.