Selected Publications
Book
Y. Thomas Hou, Yi Shi, and Hanif D. Sherali, Applied Optimization Methods for Wireless Networks, Cambridge University Press, 2014. ISBN: 9781139088466.
Patents
Y.E. Sagduyu, J. Li, Y. Shi, A. Grushin, and Z. El-Jamous, "Systems and means for generating synthetic social media data", US Patent 10,719,779.
Y.E. Sagduyu, Z. El-Jamous, M. Ding, V. Manikonda, and Y. Shi, "Systems and means for detecting automated programs used to generate social media input", US Patent 11,005,843.
Book Chapters
Y.E. Sagduyu, Y. Shi, T. Erpek, W. Headley, B. Flowers, G. Stantchev, Z. Lu, and B. Jalaian, “Adversarial machine learning: A new threat paradigm for next-generation wireless communications,” in AI, Machine Learning and Deep Learning: A Security Perspective, edited by F. Hu, X. Hei, CRC Press, June 2023.
Y.E. Sagduyu, T. Erpek, and Y. Shi, “Adversarial machine learning for 5G communications security,” to appear in Game Theory and Machine Learning for Cyber Security, edited by C.A. Kamhoua, C.D. Kiekintveld, F. Fang, Q. Zhu, pp. 270–288, New York, NY, Wiley Publisher, September 2021.
Journal papers
Y. Shi and Y.E. Sagduyu, "Membership inference attack and defense for wireless signal classifiers with deep learning," to appear in IEEE Transactions on Mobile Computing.
Y. Shi, Y.E. Sagduyu, T. Erpek, and M.C. Gursoy, “How to attack and defend 5G radio access network slicing with reinforcement learning,” IEEE Open Journal of Vehicular Technology, Special Issue - Recent Advances in Security and Privacy for 6G Networks, vol. 4, pp. 181–192, 2023.
Y. Shi, K. Davaslioglu, and Y.E. Sagduyu, "Generative adversarial network in the air: Deep adversarial learning for wireless signal spoofing," IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 1, pp. 294–303, March 2021.
Y.E. Sagduyu, Y. Shi, and T. Erpek, "Adversarial deep learning for over-the-air spectrum poisoning attacks," IEEE Transactions on Mobile Computing, vol. 20, no. 2, pp. 306–319, February 2021.
Canming Jiang, Yi Shi, Y. Thomas Hou, and Sastry Kompella, "Bicriteria optimization in multi-hop wireless networks: Characterizing the throughput-energy envelope," IEEE Transactions on Mobile Computing, vol. 12, issue 8, pp. 1866-1878, September 2013. (Selected as the Spotlight Paper for the September 2013 issue).
Conference papers:
Y. Shi, Y.E. Sagduyu, and T. Erpek, “Jamming attacks on decentralized federated learning in General Multi-Hop Wireless Networks,” in Proceedings of IEEE INFOCOM Workshop on 5G and Beyond Wireless Security (Wireless-Sec), Hoboken, NJ, May 20, 2023.
Y. Shi and Y.E. Sagduyu, “How to launch jamming attacks on federated learning in NextG wireless networks,” in Proceedings of IEEE Globecom Workshop on 5G and Beyond Wireless Security (Wireless-Sec), pp. 945–950, Rio de Janeiro, Brazil, December 4–8, 2022.
Y. Shi, M. Mehedint, T. Nguyen, and H. Zeng, “Vulnerability analysis for deep learning systems in network security,” in Proceedings of IEEE Military Communications Conference (Milcom), Restricted Program, Rockville, MD, November 28–December 2, 2022.
Y. Shi and Y.E. Sagduyu, “Sensing-throughput tradeoffs with generative adversarial networks for NextG spectrum sharing,” in Proceedings of IEEE Milcom Workshop on 5G Military Communications: Open Modular Architectures, Testbeds, and Cybersecurity, Rockville, MD, November 28, 2022.
Y. Shi, Y.E. Sagduyu, T. Erpek, and M.C. Gursoy, “Jamming attacks on NextG radio access network slicing with reinforcement learning,” in Proceedings of IEEE Future Networks World Forum (FNWF), pp. 397–402, Montreal, Canada, October 12–14, 2022.
M. Hegarty, Y.E. Sagduyu, T. Erpek, and Y. Shi, “Deep learning for spectrum awareness and covert communications via unintended RF emanations,” in Proceedings of ACM WiSec Workshop on Wireless Security and Machine Learning (WiseML), pp. 27–32, San Antonio, TX, May 19, 2022.
Y. Shi, Y.E. Sagduyu, and T. Erpek, “Federated learning for distributed spectrum sensing in NextG communication networks,” in SPIE Defense + Commercial Sensing, Orlando, vol. 12113, pp. 472–478, Orlando, FL, April 3–7, 2022.
B. Kim, Y. Shi, Y.E. Sagduyu, T. Erpek, and S. Ulukus, "Adversarial attacks against deep learning based power control in wireless communications," in Proceedings of IEEE Globecom Workshop on 5G and Beyond Wireless Security (IEEE Wireless-Sec), 6 pages, Madrid, Spain, December 7, 2021.
Y. Shi and Y.E. Sagduyu, "Adversarial machine learning for flooding attacks on 5G radio access network slicing,“ in Proceedings of IEEE ICC Workshop on 5G and Beyond Wireless Security (Wireless-Sec), 6 pages, Montreal, Canada, June 14, 2021.
Y. Shi, Y.E. Sagduyu, K. Davaslioglu, and J.H. Li, "Active deep learning attacks under strict rate limitations for online API calls," in Proceedings of IEEE International Symposium on Technologies for Homeland Security (HST), 6 pages, Crystal City, VA, May 2-3, 2018. (Best Paper Award).
Huacheng Zeng, Y. Thomas Hou, Yi Shi, Wenjing Lou, Sastry Kompella, and Scott F. Midkiff, "Shark-IA: An interference alignment algorithm for multi-hop underwater acoustic networks with large propagation delays," in Proc. ACM International Conference on Underwater Networks & Systems (WUWNet), article 6, Rome, Italy, November 12-14, 2014. (Best Student Paper Award).
Yi Shi, Jia Liu, Canming Jiang, Cunhao Gao, and Y. Thomas Hou, "An optimal link layer model for multi-hop MIMO networks," in Proc. IEEE INFOCOM, pp. 1916-1924, Shanghai, China, April 10-15, 2011. (The only Best Paper Award Runner-Up).
Yi Shi, Liguang Xie, Y. Thomas Hou, and Hanif D. Sherali, "On renewable sensor networks with wireless energy transfer," in Proc. IEEE INFOCOM, pp. 1350-1358, Shanghai, China, April 10-15, 2011. (2023 INFOCOM Test of Time Paper Award).
Yi Shi and Y. Thomas Hou, "Theoretical results on base station movement problem for sensor network," in Proc. IEEE INFOCOM, pp. 376-384, Phoenix, AZ, April 14-17, 2008. (Best Paper Award).
Last updated: April 24, 2023