Qiang Zeng

I am an Assistant Professor in the Department of Computer Science and Engineering at University of South Carolina. I received my Ph.D. from Penn State University, and my Bachelor's and Master's degrees from Beihang University. The easiest way to contact me is through email at zeng1 (at) cse.sc.edu.

My main research interest is Computer Systems Security, with a focus on Cyber-Physical Systems, Internet of Things, and Mobile Computing. I also work on the security aspect of machine learning. I have rich industry experience and have worked in the IBM T.J. Watson Research Center, the NEC Lab America, Symantec, and Yahoo.

Our lab is recruiting PhD, Master, Undergraduate Students, and Post-Doctoral Researchers.

Our HBCU-USC Collaborative Lab looks for students from underrepresented groups to join us.

Services (selected):


11/06/21 Our work revealing novel attacks with impacts on tens of billions of devices is accepted to S&P'22.

08/28/21 Our paper that presents the first smart app fuzzing system in the literature is accepted to ACSAC'21.

08/12/21 My fourth-year PhD student, Fei Zuo, passed his dissertation defense. Congratulations, Fei!

07/02/21 Our work that protects IoT privacy against sniffing attacks is accepted to RAID'21.

05/18/21 Our Medium proposal about IoT research in multi-platform environments is funded by NSF.

12/22/20 Our work that protects the privacy of smart home users from IoT platforms without impairing home automation is accepted to NDSS'21.

10/24/20 Our work that detects adversarial examples simply by erasing and restoring some randomly selected pixels is accepted to AsiaCCS'21 (acceptance rate 18.5% in Round One).

09/30/20 Our work that detects IoT attacks and malfunctions without modifying IoT firmware is accepted to USENIX Security'21 .

08/17/20 Our work about attacking graph-based classification is accepted to ACSAC'20.

07/28/20 Our work about secure and usable IoT pairing is accepted to CCS'20.

07/09/20 Our Medium proposal about building IoT Testbeds is funded by NSF.

03/04/20 Our work that, being the first in the literature, systematically categorizes and detects cross-app interference threats in appified smart environments is accepted to DSN'20 (acceptance rate 16.5%).

07/11/19 A novel IoT authentication work is accepted to MobiCom'19.

05/29/19 AEPecker, which not only detects adversarial examples but also rectifies the classification results, is accepted to RAID'19.

03/04/19 Our work that detects audio adversarial examples at accuracies over 99% is accepted to DSN'19.

03/04/19 Our work that can automatically patch for (almost) ALL heap vulnerabilities without changing the binary code is accepted to DSN'19.

11/06/18 Our work that pioneers the direction of Natural Language Processing Inspired Binary Code Analysis is accepted to NDSS'19.

10/01/18 Our proposal about Insecurity Analysis of Middleware on Mobile Platforms is funded by NSF.