Bo Yuan
Professor
Department of Cybersecurity
Golisano College of Computing and Information Sciences
585-475-4468
Office Location
Office Mailing Address
100 Lomb Memorial Drive Rochester, NY 14623 70-3706
Bo Yuan
Professor
Department of Cybersecurity
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 in the Cybersecurity Department at Rochester Institute of Technology. He was the department chair from 2014 to 2022 and oversaw the tremendous growth of the department and the cybersecurity programs. His research areas are computational intelligence and its application in cybersecurity. Dr. Yuan is the PI of multiple cybersecurity grants, including the CyberCorps (R) Scholarship for Service grant funded by the National Science Foundation (NSF) and the DoD Cyber Scholarship Program. He is the POC of CAE-CD and CAE-R at RIT. Before he joined RIT in 2003, Dr. Yuan was a staff scientist at Manning & Napier Information Services. He received a Ph.D. in Systems Science from Binghamton University in 1996.
585-475-4468
Areas of Expertise
Cybersecurity
Malware
Computational Intelligence
Machine Learning
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-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-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.