Clement Fung
PhD Student
Carnegie Mellon University

Selected Publications



Attributions for ML-based ICS Anomaly Detection: From Theory to Practice
Clement Fung, Eric Zeng, Lujo Bauer.
31st Network and Distributed System Security Symposium (NDSS 2024)
San Diego, CA, USA. February 2024.
[PDF] [Slides] [Code]


Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems
Clement Fung, Shreya Srinarasi, Keane Lucas, Hay Bryan Phee, Lujo Bauer.
27th European Symposium on Research in Computer Security (ESORICS 2022)
Copenhagen, Denmark. September 2022.
[PDF] [Springer] [Video] [Slides] [Code]


Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
Muhammad Shayan, Clement Fung, Chris J.M. Yoon, Ivan Beschastnikh.
IEEE Transactions on Parallel and Distributed Systems (TPDS)
Volume 32, Issue 7. July 2021.
[PDF] [IEEE] [Code]


Towards a Lightweight, Hybrid Approach for Detecting DOM XSS Vulnerabilities with Machine Learning
William Melicher, Clement Fung, Lujo Bauer, Limin Jia.
The Web Conference 2021
Ljubjana, Slovenia (Virtual). April 2021.
[PDF] [Video] [Code]


The Limitations of Federated Learning in Sybil Settings
Clement Fung, Chris J.M. Yoon, Ivan Beschastnikh.
23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020)
Donostia/San Sebastian, Spain (Virtual). October 2020.
[PDF] [Video] [Slides] [Code]


Brokered Agreements in Multi-Party Machine Learning
Clement Fung, Ivan Beschastnikh.
10th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2019)
Hangzhou, China. August 2019.
[PDF] [ACM] [Slides] [Code]


GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery
David Dao, Catherine Cang, Clement Fung, Ming Zhang, Nick Pawlowski, Reuven Gonzales, Nick Beglinger, Ce Zhang.
Climate Change: How Can AI Help?: ICML 2019 Workshop
Long Beach, CA, USA. June 2019.
[PDF] [Poster]

Pre-prints



Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data
Clement Fung, Chen Qiu, Aodong Li, Maja Rudolph.
arXiv preprint 2310.10461, October 2023.
[PDF]


Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting
Clement Fung, Jaime Koerner, Stewart Grant, Ivan Beschastnikh.
arXiv preprint 1811.09712, November 2018.
[PDF] [Code]


Mitigating Sybils in Federated Learning Poisoning
Clement Fung, Chris J.M. Yoon, Ivan Beschastnikh.
arXiv preprint 1808.04866, August 2018.
[PDF] [Code]

Posters



Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
Muhammad Shayan, Clement Fung, Chris J.M. Yoon, Ivan Beschastnikh.
NSDI 2019 Poster Session
Boston, MA, USA. February 2019.
[PDF]

Other External Talks



Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems
Center of Academic Excellence in Cyber Research (CAE-R) Seminar, Virtual, USA. April 2023.


Detecting and Explaining Anomalies in Industrial Control
2022 CyLab Partners Conference, Pittsburgh, PA, USA. October 2022.


Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting
UBC Cybersecurity Summit, Vancouver, BC, Canada. May 2018.
[Video] [Poster]


Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting
University of Toronto, Toronto, ON, Canada. December 2017.
[Link]