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. I am a recipient of an NSF CAREER Award.

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 Adversarial Machine Learning.

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.

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News:

12/14/21 I received an NSF CAREER Award.

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.