Tianrui Hu

I recently earned my PhD from the Khoury College of Computer Sciences at Northeastern University, advised by Prof. David Choffnes and Dr. Daniel Dubois. My research lies in computer network systems, primarily focusing on IoT privacy, security, and network-based measurement, with a broader research interest in Internet measurement, networking, anomaly detection, and security & privacy of networked system. Before joining Northeastern, I obtained my M.S. from Virginia Tech under the supervision of Prof. Gang Wang and Prof. Bimal Viswanath.

Experience

• Google

  Software Engineer 05/2025 - Present
  

• Netflix

  Security Engineer Intern 06/2023 - 08/2023
  

Education

• Northeastern University

  Ph.D. in Computer Science 09/2020 - 04/2025

• Virginia Tech

  M.S. in Computer Science 08/2018 - 05/2020

• Beijing University of Posts and Telecommunications

  B.Eng. in Telecommunication Engineering 09/2014 - 06/2018

Publications

• [IMC ‘24] IoT Bricks Over v6: Understanding IPv6 Usage in Smart Homes

  [PDF] [Dataset & Code] Proceedings of the ACM Internet Measurement Conference (IMC), 2024. Tianrui Hu, Daniel J. Dubois, David Choffnes. * Presentation at:
  - RIPE 89 Measurement Analysis and Tools Working Group (Oct 29th)
  - IETF 121 Dublin IRTF Measurement and Analysis for Protocols Research Group (Nov 5th)

• [IMC ‘23] BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models

  [PDF] [Dataset & Code] Proceedings of the ACM Internet Measurement Conference (IMC), 2023. Tianrui Hu, Daniel J. Dubois, David Choffnes.

• [IMC ‘23] In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes

  [PDF] [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. Impact: Our findings are acknowledged by several major IoT vendors and Google, who are working with us on potential mitigations through Android OS, app review, and IoT standardization. News coverage: [CBS] [Wired(ES)] [El País] [Northeastern] [Reddit]

• [IEEE S&P ‘20] Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation

  [PDF] 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.

• [IC-NIDC ‘18] Multi-Task Adversarial Network Bottleneck Features for Noise-Robust Speaker Verification

  [PDF] 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.

• [IJCAI ‘17] Depression detection via harvesting social media: A multimodal dictionary learning solution

  [PDF] 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.

Research Projects

• Mon(IoT)r: Network-based Approaches to Understand and Improve Privacy and Security of Smart Home IoT Devices | 2020-present
• ProperData: Protecting Personal Data Flow on the Internet | 2020-present

• Netflix Intern Project| 2023.6-2023.8

  - Developed a machine learning-based, data-driven system for network behavior modeling and anomaly detection to more effectively model client network behaviors and detect unexpected activities (e.g., abuse, fraud, piracy), improving mean time to detect (MTTD), reducing false negative/positive rates (FNR, FPR), and minimizing manual effort.
  - Explored the use of AI language models for network behavior modeling and anomaly detection, enhancing the detection of unexpected patterns and improving network behavior insights.
  
• 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