Bo Yuan Headshot

Bo Yuan

Professor

Department of Computing Security
Golisano College of Computing and Information Sciences
bo.yuan@rit.edu
585-475-4468
Office Location
GCI-3705
Office Mailing Address
100 Lomb Memorial Drive Rochester, NY 14623 70-3706

Bo Yuan

Professor

Department of Computing Security
Golisano College of Computing and Information Sciences

Education

BS, MS, Shanghai Normal University (China); Ph.D., State University of New York at Binghamton

Bio

Bo Yuan is a professor and chair of Computing Security Department at Rochester Institute of Technology. His main interests are in cybersecurity education and research. His research areas are in computational intelligence and its application in cybersecurity. Dr. Yuan is the PI of multiple cybersecurity grants including the five-year, $4 million CyberCorps (R) Scholarship for Service grant funded by National Science Foundation (NSF). He is also the associate director of the Center for Cybersecurity at RIT and has led to RIT's recent successful designation as CAE-CD and CAE-R by NSA. Dr. Yuan is the current chair of IEEE Rochester joint chapters of computer and computational intelligence societies. Before he joined RIT in 2003, Dr. Yuan was a staff scientist at Manning & Napier Information Services for six years. He received a Ph.D. in system science from Binghamton University in 1996.

bo.yuan@rit.edu
585-475-4468
Personal Links
Personal Website
Google Scholar
Areas of Expertise
Artificial Intelligence
Security and Privacy

Select Scholarship

Published Conference Proceedings
Mosli, Rayan, et al. "A Behavioral-Based Approach for Detecting Malware." Proceedings of the Thirteenth IFIP WG 11.9 International Conference on Digital Forensics, Orlando, FL, 2017. Ed. Gilbert Peterson and Sujeet Shenoi. Orlando, FL: Springer, 2017. Print.
Mosli, Rayan, et al. "Automated Malware Detection Using Artifacts in Forensic Memory Images." Proceedings of the IEEE Symposium on Technologies for Homeland Security (HST). Ed. IEEE. Waltham, MA: IEEE, 2016. Print.
Huba, William, et al. "Towards A Web Tracking Profiling Algorithm." Proceedings of the IEEE International Conference on Technologies for Homeland Security. Ed. Israel Soibelman. Waltham, MA: n.p., 2013. Print.
Schellenberg, Thomas, Bo Yuan, and Richard Zanibbi. "Layout-based Substitution Tree Indexing and Retrieval for Mathematical Expressions." Proceedings of the Document Recognition and Retrieval XIX. Ed. Christian Viard-Gaudin and Richard Zanibbi. Burlingame, California: Proc. SPIE 8297, 2012. Print.
Huba, William, et al. "A HTTP Cookie Covert Channel." Proceedings of the SIN '11 4th International Conference on Security of Information and Networks, 14-19 November 2011, Sydney, Australia. Ed. Mehmet A. Orgun, et al. New York, NY: ACM, 2011. Print.
Zanibbi, Richard and Bo Yuan. "Keyword and image-based retrieval for mathematical expressions." Proceedings of the Proc. Document Recognition and Retrieval XVIII. Ed. Gady Agam and Christian Viard-Gaudin. San Francisco, CA: SPIE, 2011. Print.
Book Chapter
Pan, Yin, Bo Yuan, and Sumita Mishra. "Network Security Auditing." Network Security, Administration and Management: Advancing Technology and Practice. Ed. Dulal Chandra Kar and Mahbubur Rahman Syed. Hershey: IGI Global, 2011. 131-157. Print.
Journal Paper
Brown, Erik, et al. "Covert Channels in the HTTP Network Protocol: Channel Characterization and Detecting Man-in-the-Middle Attacks." The Journal of Information Warfare 9. 3 (2010): 26-38. Print.

Currently Teaching

CSEC-472
Authentication and Security Models
3 Credits
Access control and authentication systems are some of the most critical components of cybersecurity ecosystems. This course covers the theory, design, and implementation of systems used in identification, authentication, authorization, and accountability processes with a focus on trust at each layer. Students will examine formal models of access control systems and approaches to system accreditation, the application of cryptography to authentication systems, and the implementation of IAAA principles in modern operating systems. A special focus will be placed on preparing students to research and write about future topics in this area.
CSEC-490
Capstone in Computing Security
3 Credits
This is a capstone course for students in the information security and forensics program. Students will apply knowledge and skills learned and work on real world projects in various areas of computing security. Projects may require performing security analysis of systems, networks, and software, etc., devising and implementing security solutions in real world applications.
CSEC-520
Cyber Analytics and Machine Learning
3 Credits
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.
CSEC-599
Independent Study in CSEC
1 - 6 Credits
Students will work with a supervising faculty member on a project of mutual interest. Project design and evaluation will be determined through discussion with the supervising faculty member and documented through completion of an independent study form to be filed with the department of computing security.
CSEC-620
Cyber Analytics and Machine Learning
3 Credits
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.
CSEC-759
Graduate Seminar in Computing Security
3 Credits
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.

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