Liyang Zhang

Education

Ph.D. in Electrical and Computer Engineering - Northeastern University (2019)
M.S. in Electrical Engineering - State University of New York at Buffalo (2014)
B.S. in Electrical Engineering - Tsinghua University, Beijing, China (2008)

Liyang Zhang earned his Ph.D. in Electrical and Computer Engineering from Northeastern University in 2019. He received his M.S. in Electrical Engineering from State University of New York at Buffalo in 2014, and B.S. in Electrical Engineering from Tsinghua University, Beijing, China in 2008. From 2008 to 2011 he was an engineer at Infinova Technology in Shenzhen, China. He was an intern at Huawei Research Center, Santa Clara, CA from November 2016 to May 2017. He worked in the Wireless Networks and Embedded Systems Laboratory under Professor Tommaso Melodia. His research interests included wireless security, wireless communication and networking theory, Internet of Things, and machine learning. After graduation, he joined Google’s Network Infrastructure Team as a Software Engineer.

Publications

2019

Journals & Magazines

L. Zhang, F. Restuccia, T. Melodia, and S. Pudleswki. “Jam Sessions: Analysis and Experimental Evaluation of Advanced Jamming Attacks in MIMO Networks.” Proc. of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) (2019)Journal

DOI

Book Chapters

F. Restuccia, S. D'Oro, L. Zhang, and T. Melodia. “The Role of Machine Learning and Radio Reconfigurability in the Quest for Wireless Security.” Proactive and Dynamic Network Defense (2019)Book Chapter

DOI Link

Wireless networks require fast-acting, effective and efficient security mechanisms able to tackle unpredictable, dynamic, and stealthy attacks. In recent years, we have seen the steadfast rise of technologies based on machine learning and software-defined radios, which provide the necessary tools to address existing and future security threats without the need of direct human-in-the-loop intervention. On the other hand, these techniques have been so far used in an ad hoc fashion, without any tight interaction between the attack detection and mitigation phases. In this chapter, we propose and discuss a Learning-based Wireless Security (LeWiS) framework that provides a closed-loop approach to the problem of cross-layer wireless security. Along with discussing the LeWiS framework, we also survey recent advances in cross-layer wireless security.

2018

Journals & Magazines

L. Zhang, F. Restuccia, T. Melodia, and S. Pudlewski. “Taming Cross-Layer Attacks in Wireless Networks: A Bayesian Learning Approach.” IEEE Transactions on Mobile Computing (2018)Journal

DOI

2017

Journals & Magazines

L. Zhang, F. Restuccia, T. Melodia, and S. Pudlewski. “Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications.” Proc. of IEEE Conf. on Communications and Network Security (2017)Journal

DOI

Conference Papers

L. Zhang, S. Amin, and C. Westphal. “VR Video Conferencing over Named Data Networks.” Proc. of the Workshop on Virtual Reality and Augmented Reality Network (2017)Conference

DOI

L. Zhang, S. Amin, and C. Westphal. “Demo: VR Video Conferencing over Named Data Networks.” Proc. of ACM Conference on Information-Centric Networking (ICN) (2017)Conference

DOI

2016

Journals & Magazines

L. Zhang, Z. Guan, and T. Melodia. “United Against the Enemy: Anti-Jamming Based on Cross-Layer Cooperation in Wireless Networks.” IEEE Transactions on Wireless Communications (2016)Journal

DOI

2015

Conference Papers

L. Zhang and T. Melodia. “Hammer and anvil: The threat of a cross-layer jamming-aided data control attack in multihop wireless networks.” Proc. of IEEE Conference on Communications and Network Security (CNS) (2015)Conference

DOI

2014

Conference Papers

L. Zhang, Z. Guan, and T. Melodia. “Cooperative Anti-jamming for Infrastructure-less Wireless Networks with Stochastic Relaying.” Proc. of IEEE Conference on Computer Communications (INFOCOM) (2014)Conference

DOI