Dr. Xiangyang Li

Email Address


TEACHING

EN.650.601 Introduction to Information Security (Fall)

EN.650.654 Computer Intrusion Detection (Spring)

EN.650.658 Introduction to Cryptography (Fall)


Research

Security Analysis and Intelligence

Human Security Informatics

User Modeling


Current/RECENT Projects

https://www.unknot.id/post – Implicit Private Authentication with Ultrasonic Signals from Mobile Ecosystem

http://blogs.cofc.edu/cyberpaths/ – Broadening the Path to the STEM Profession Through Cybersecurity Learning

http://behavior.isi.jhu.edu/ – Computational Cognitive Modeling of User Security and Incentive Behaviors


Recent Publications

Q. Cheng, A. Xu, X. Li, and L. Ding, “Adversarial Email Generation against Spam Detection Models through Feature Perturbation,” The 2022 IEEE International Conference on Assured Autonomy (ICAA’22), Virtual Event, March 22-23, 2022. [Download]

J. He, Q. Cheng, and X. Li, “Understanding the Impact of Bad Words on Email Management through Adversarial Machine Learning,” SIG-KM International Research Symposium 2021, Virtual Event, The University of North Texas, September 29, 2021. (First Place Award) [Download]

C. Wang, D. Zhang, S. Huang, X. Li, and L. Ding, “Crafting Adversarial Email Content against Machine Learning Based Spam Email Detection,” In Proceedings of the 2021 International Symposium on Advanced Security on Software and Systems (ASSS ’21) with AsiaCCS 2021, Virtual Event, Hong Kong, June 7, 2021. [Download]

M. Kang, M. Shonman, A. Subramanya, H. Zhang, X. Li, and A. Dahbura, “Understanding Security Behavior of Real Users: Analysis of a Phishing Study,” 2021 Hawaii International Conference on System Sciences (HICSS-54), Virtual Event, January 5-8, 2021. [Download]

H. Guo, Z. Wang, B. Wang, X. Li, and D. Shila, “Fooling A Deep-Learning Based Gait Behavioral Biometric System,” The Workshop on Assured Autonomous Systems (WAAS) with the IEEE Symposium on Security and Privacy (S&P), Virtual Event, May 21, 2020. [Download]

Y. Li, K. Xiong, and X. Li, “Applying Machine Learning Techniques to Understand User Behaviors When Phishing Attacks Occur,” EAI Endorsed Transactions on Security and Safety, Vol. 19, No. 21, 2019. [Download]

Y. Li, K. Xiong, and X. Li, “An Analysis of User Behaviors in Phishing Email Using Machine Learning Techniques,” The 16th International Conference on Security and Cryptography (SECRYPT 2019), Prague, Czech Republic, July 26-28, 2019.[Download]

H. Zhang, D. Singh, and X. Li, “Augmenting Authentication with Context-Specific Behavioral Biometrics,” 2019 Hawaii International Conference on System Sciences (HICSS-52), Grand Wailea, Hawaii, January 8-11, 2019. [Download]

J. Zhang, Y.-T. Jou, and X. Li, “Cross-Site Scripting (XSS) Detection Integrating Evidences in Multiple Stages,” 2019 Hawaii International Conference on System Sciences (HICSS-52), Grand Wailea, Hawaii, January 8-11, 2019. (Best Paper Nomination) [Download]

M. Shonman, X. Li, H. Zhang, and A. Dahbura, “Simulating Phishing Email Processing with Instance-Based Learning and Cognitive Chunk Activation,” The 11th International Conference on Brain Informatics (BI 2018), Arlington, Texas, December 7-9, 2018. [Download]

H. Zhang, S. Singh, X. Li, A. Dahbura and M. Xie, “Multitasking and Monetary Incentive in a Realistic Phishing Study,” The 32nd Human Computer Interaction Conference (British HCI-2018), Belfast, Northern Ireland, July 2-6, 2018. [Download]

X. Mountrouidou, X. Li, and Q. Burke, “Cybersecurity in Liberal Arts General Education Curriculum,” The 23rd Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), Larnaca, Cyprus, July 2-4, 2018.

X. Mountrouidou, X. Li, and Q. Burke, “Cyber Security Education for Liberal Arts Institutions,” The 22nd Colloquium for Information Systems Security Education (CISSE 2018), New Orleans, Louisiana, June 11-13, 2018.


Updated April 2022

JHU Information Security Institute