Tianrui Hu

I am a fourth-year Ph.D. Candidate in the Khoury College of Computer Sciences at Northeastern University, working with Prof. David Choffnes. My current research primarily focuses on IoT privacy & security and internet measurement, with a broader interest in privacy and security of networked system.

In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes [dataset & code] Proceedings of the ACM Internet Measurement Conference (IMC), 2023. Aniketh GirishASTERISK_PLACEHOLDER, Tianrui HuASTERISK_PLACEHOLDER, Vijay Prakash, Daniel J. Dubois, Srdjan Matic, Danny Yuxing Huang, Serge Egelman, Joel Reardon, Juan Tapiador, David Choffnes, Narseo Vallina-Rodriguez. ASTERISK_PLACEHOLDER Equal contribution.
BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models [dataset & code] Proceedings of the ACM Internet Measurement Conference (IMC), 2023. Tianrui Hu, Daniel J. Dubois, David Choffnes.
Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation Proceedings of The 41st IEEE Symposium on Security and Privacy (IEEE S&P), 2020. Steve T.K. Jan, Qingying Hao, Tianrui Hu, Jiameng Pu, Sonal Oswal, Gang Wang, Bimal Viswanath.
Multi-Task Adversarial Network Bottleneck Features for Noise-Robust Speaker Verification Proceedings of the 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC), 2018. Hong Yu, Tianrui Hu, Zhanyu Ma, Zheng-Hua Tan, Jun Guo.
Depression detection via harvesting social media: A multimodal dictionary learning solution Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017. Guangyao Shen, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, Wenwu Zhu.

Mon(IoT)r: Privacy and Security Analysis of Smart Home IoT Devices | 2020-present
ProperData: Protecting Personal Data Flow on the Internet | 2020-present
Netflix Intern Project: Fight Content Piracy and App Abuse/Misuse using Data-driven Approach | 2023.6-2023.8
BehavIoT: Modeling Smart Home Behavior | 2020-2022
Detecting Bots with Limited Data using Neural Data Augmentation | under the supervision of Prof. Gang Wang and Prof. Bimal Viswanath | 2018-2020
- Build a stream-based real-time bot detection system to complement with rule-based method to catch advanced bots through network traffic.
- Develop a data synthesis method to enable effective ML-based detection model training with limited labeled data.
- Validate our system using real-world datasets from 3 different online services and sustain our system over a long period of time with low-cost retraining.
- Explore the relationship between data synthesis and adversarial re-training.

胡天睿