PhD student in Computer Science
I am a fifth year computer science PhD student at Northeastern University, advised by David Choffnes. I have a broad interest in understanding the Internet and improving the user experience. Recently, I have been working on identifying traffic differentiation, how network traffic are classified and possible ways to avoid being classified.
Before joining Northeastern, I received my M.S. in Computer Engineering at Duke University (Let's go Blue Devils!) , and spent four amazing years completing my B.S. in Electronic Engineering at UESTC, ChengDu, China. My Resume can be found here
- Have you ever wondered if your ISP is slowing down certain traffic relative to others? Unfortunately, you mobile device and carriers currently give you little or no way to tell if this is the case. In fact, with the recent repealing of the previous FCC rules for net neutrality, it is possibly legal for mobile carriers to block, shape or modify nearly any kind of network traffic. With the mobile app Wehe, we give you a way to test if this is happening to your traffic.
- liberate, a general-purpose tool for automatically identifying middlebox policies, reverse-engineering their implementations, and adaptively deploying custom circumvention techniques. liberate conducts targeted network measurements to identify the corresponding inconsistencies and leverages this information to transform arbitrary network traffic such that it is purposefully misclassified (e.g., to avoid shaping or censorship).
- Classifiers Unclassified
- In this work, we develop a general approach for identifying classification rules (i.e., the network provider's "educated guesses") that map network traffic to applications. We also characterize the classification rules for HTTP(S) traffic implemented in today's carrier-grade middleboxes and identify examples of misclassification (traffic from application A being labeled mistakenly as application B). In summary, our analysis shows that different vendors use different matching rules, but all generally focus on a small number of fields inside HTTP/S traffic.
- A Large-Scale Analysis of Deployed Traffic Differentiation Practices, PDF
Fangfan Li, Arian Akhavan Niaki, David Choffnes, Phillipa Gill, Alan Mislove. In Proceedings of the ACM SIGCOMM, Beijing, China, August 2019.
- lib.erate,(n): A library for exposing (traffic-classification) rules and avoiding them
Fangfan Li, Abbas Razaghpanah, Arash Molavi Kakhki, Arian Akhavan Niaki, David Choffnes, Phillipa Gill, Alan Mislove. In Proceedings of the 17th ACM Internet Measurement Conference IMC'17, London, UK, November 2017.
- Classifiers Unclassified: An Efficient Approach to Revealing IP Traffic Classification Rules, PDF
Fangfan Li, Arash Molavi Kakhki, David Choffnes, Phillipa Gill, Alan Mislove. In Proceedings of the 16th ACM Internet Measurement Conference IMC'16, Santa Monica, CA, November 2016.
- Binge On Under the Microscope: Understanding T-Mobile’s Zero-Rating Implementation, PDF
Arash Molavi Kakhki, Fangfan Li, David Choffnes, Alan Mislove, Ethan Katz-Bassett. In Proceedings of SIGCOMM Internet-QoE Workshop, August 2016.
Email: firstname.lastname@example.org. If you are on campus, you can probably find me in the office ISEC 6th floor.