Chao Feng

Chao Feng is pursuing a Ph.D. in the Department of Informatics at the University of Zurich. He obtained his MSc. in Informatics from the University of Zurich and a BSc. in Management Information System from Renmin University of China. Chao is currently working as a Ph.D. student and Junior Researcher in the Communication Systems Group, supervised by Prof. Dr. Burkhard Stiller. His research primarily focuses on enhancing the cybersecurity of Internet-of-Things (IoT) and improving the robustness of Decentralized Distributed Machine Learning systems,  especially for Decentralized Federated Learning (DFL).

• PMMK: Protocols for Multi-media Communications (HS22)
• IntEco: Internet Economics Seminar (HS22, HS23)
• CESS: Computer Engineering and Software Systems (FS23)
• ComSys: Communication Systems (FS23)

• CNSP: Computer Network Security Principles (HS23)

Cybersecurity
Internet of Things (IoT)
Data Mining and Artificial Intelligence

Please check here to see list of open thesis. If you are interested to work on a project in the list mentioned above or any other project related to my research area feel free to contact me.

2025

• [Full Paper] Chao Feng, Alberto Huertas Celdran, Pedro Miguel Sanchez Sanchez, Jan Kreischer, Jan von der Assen, Gerome Bovet, Gregorio Martinez Perez, Burkhard Stiller: CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation; IEEE, IEEE Transactions on Dependable and Secure Computing, 2025, pp 1–14. URL
• [Preprint] Chao Feng, Alberto Huertas Celdran, Xi Cheng, Gérôme Bovet, Burkhard Stiller: GreenDFL: a Framework for Assessing the Sustainability of Decentralized Federated Learning Systems; arxiv, arxiv, Zürich, Switzerland, February 2025, URL
• [Preprint] Chao Feng, Yuanzhe Gao, Alberto Huertas Celdran, Gerome Bovet, Burkhard Stiller: From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning;  arxiv, Zürich, Switzerland, January 2025, URL
• [Preprint] Chao Feng, Nicolas Fazli Kohler, Alberto Huertas Celdran, Gerome  Bovet, Burkhard Stiller:  ColNet: Collaborative Optimization in Decentralized Federated Multi-task  Learning Systems ;  arxiv, Zürich, Switzerland, January 2025, URL
• [Full Paper] Xiao Chen, Chao Feng, Shaohua Wang: AIDFL: An Information-Driven Anomaly Detector for Data Poisoning in Decentralized Federated Learning; IEEE, IEEE Access, Vol. 1, No. 1, March 2025, pp 1–15. URL
• [Short Paper] Alberto Huertas Celdran, Chao Feng, Sabyasachi Banik, Gerome Bovet, Gregorio Martinez Perez, Burkhard Stiller: De-VertiFL: A Solution for Decentralized Vertical Federated Learning; 38th IEEE/IFIP Network Operations and Management Symposium (NOMS 2025), Honolulu, HI, USA, May 2025
• [Preprint] Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Chao Feng, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán: S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning; arxiv, Zürich, Switzerland, January 2025, URL

2024

• [Full Paper] Chao Feng, Alberto Huertas Celdran, Zien Zeng, Zi Ye, Jan von der Assen, Gerome Bovet, Burkhard Stiller. "Leveraging MTD to Mitigate Poisoning Attacks in Decentralized FL with Non-IID Data." 2024 IEEE International Conference on Big Data (IEEE BigData 2024), Dec 2024, Washington DC, USA, pp.1-10 (To appear, arxiv arXiv.2409.19302)
• [Preprint] Chao Feng, Hongjie Guan, Alberto Huertas Celdrán, Jan von der Assen, Gérôme Bovet, Burkhard Stiller. "FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning." arXiv, 2024
• [Full Paper] Chao Feng, Alberto Huertas Celdran, Michael Vuong, Gerome Bovet, Burkhard Stiller. "Voyager: MTD-Based Aggregation Protocol for Mitigating Poisoning Attacks on DFL." NOMS 2024 IEEE Network Operations and Management Symposium, Seoul, Korea, Republic of, 2024, pp.1-9
• [Full Paper] Chao Feng, Alberto Huertas Celdran, Janosch Baltensperger, Enrique Tomas Matınez Bertran, Gerome Bovet, Burkhard Stiller. "Sentinel: An Aggregation Function to Secure Decentralized Federated Learning." European Conference on Artificial Intelligence, Santiago de Compostela, Spain, October 2024, pp.1-6 
• [Full Paper] Chao Feng, Alberto Huertas Celdran, Jan von der Assen, Enrique Tomás Martínez Beltrán， Gerome Bovet, Burkhard Stiller. "DART: a Module for Decentralized Federated
  Learning Robustness Analysis." Elsevier ARRAY, 2024
• [Full Paper] Alberto Huertas Celdran, Chao Feng, Pedro Miguel Sanchez Sanchez, Lynn Zumtaugwald, Gerome Bovet, and Burkhard Stiller. "Assessing the Sustainability and Trustworthiness of Federated Learning Models." Sustainable Computing: Informatics and Systems, 2024, pp.1-12 (Under Review)
• [Full Paper] Muriel Figueredo Franco, Fabian Künzler, Jan von der Assen, Chao Feng, Burkhard Stiller. "RCVaR: an Economic Approach to Estimate Cyberattacks Costs using Data from Industry Reports." Computers & Security, 2024, vol.139, pp.103737.
• [Full Paper] Enrique Tomás Martínez Beltrán, Ángel Luis Perales Gómez, Chao Feng, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán. "Fedstellar: A Platform for Decentralized Federated Learning." Expert Systems with Applications, 2024, vol.242, pp.122861.
• [Full Paper] Alberto Huertas Celdrán, Jan von der  Assen, Chao Feng, Sandro Padovan, Gérôme Bovet, Burkhard Stiller: Next Generation of AI-based Ransomware, 2024 IEEE Global Communications Conference: Communication & Information Systems Security, Cape Town, South Africa, December, 2024 (To appear)
• [Full Paper] Jan von der Assen, Jamo Sharif, Chao Feng, Christian Killer, Gérôme Bovet, Burkhard Stiller: Asset-centric Threat Modeling for AI-based Systems; 2024 IEEE International Conference on Cyber Security and Resilience (CSR), London, England, September 2024, pp 1-8  (To appear)
• [Preprint] Jan von der Assen, Jamo Sharif, Chao Feng, Gérôme Bovet, Burkhard Stiller: Asset-driven Threat Modeling for AI-based Systems, arXiv e-prints (2024): arXiv:2403, March 2024
• [Full Paper] Jan von der Assen, Chao Feng, Alberto Huertas Celdran, Raffael Mogicato, Adrian Zermin, Vichhay Ok, Gérôme Bovet, Burkhard Stiller: A Lightweight Data Mining Platform for Dynamic and Reproducible Malware Analysis, 2024 11th Swiss Conference on Data Science (SDS), Zürich, Switzerland, May 2024, pp. 1-6
• [Preprint] Jan von der Assen, Chao Feng, Alberto Huertas Celdrán, Róbert Oleš, Gérôme Bovet, Burkhard Stiller: GuardFS: a File System for Integrated Detection and Mitigation of Linux-based Ransomware, Available at SSRN, February 2024
• [Preprint] Alberto Huertas Celdrán, Chao Feng, Sabyasachi Banik, Gerome Bovet, Gregorio Martinez Perez, Burkhard Stiller: De-VertiFL: A Solution for Decentralized Vertical Federated Learning, arXiv e-prints (2024)

2023

• [Full Paper] Chao Feng, Alberto Huertas Celdran, Pedro Miguel Sanchez Sanchez, Jan Kreischer, Jan von der Assen, Gerome Bovet, Gregorio Martinez Perez, and Burkhard Stiller. "CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation." IEEE Transactions on Dependable and Secure Computing, 2023, pp 1-11 (Under Review)

• [Full Paper]  Celdrán, Alberto Huertas, Pedro Miguel Sánchez Sánchez, Chao Feng, Gérôme Bovet, Gregorio Martínez Pérez, and Burkhard Stiller. "A Summary of Privacy-preserving and Syscall-based Intrusion Detection System for IoT Sensors Affected by Data Falsification Attacks." In Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023, pp. 547-548. Universidade de Vigo, 2023.

• [Full Paper] Chao Feng, Jan von der Assen, Alberto Huertas Celdrán, Steven Näf, Gérôme Bovet, Burkhard Stiller:  FeDef: A Federated Defense Framework Using Cooperative Moving Target Defense; 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech), Bol/Split, Croatia, June 2023, pp. 1-6.
• [Full Paper ] Katharina Olga Emilia Müller, Louis Bienz, Bruno Bastos Rodrigues, Chao Feng, Burkhard Stiller: HomeScout: Anti-Stalking Mobile App for Bluetooth Low Energy Devices; The 48th IEEE Conference on Local Computer Networks (LCN), Daytona Beach, Florida, U.S.A., October 2023, pp 1–8.  
• [Demo] Eryk Schiller, Chao Feng, Rafael Hengen Ribeiro, Francesco Marino, Martin Buck, Burkhard Stiller: TactSR: Utilizing SRv6 to Optimize the Routing Behavior for Tactical Networks; 2023 24th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Boston, USA, June 2023, pp. 1-3. 

2022

• [Full Paper] K. O. E. Müller, J. von der Assen, C. Feng, and B. Stiller, "An Overview and Ontology of Privacy to Preserve Privacy in Ultra-Wideband Networks," in IEEE International Conference on Privacy Computing, Haikou, China, December 2022, pp. 1-9. 
• [Full Paper] A. H. Celdrán, P. M. S. Sánchez, C. Feng, G. Bovet, G. M. Pérez and B. Stiller, "Privacy-preserving and Syscall-based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3213889. Link

• [BA] Filip Trendafilov: Implementation of Membership Inference Attack Affecting Federated Learning-based Anomaly Detection System; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, May 2023, URL: link  
• [BA] Timothy-Till Näscher: Poisoning Attack Behavior Detection in Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, May 2023, URL: link
• [BA] Michael Vuong: Design and Implementation of a Byzantine Robust Aggregation Mechanism for Decentralized Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, August 2023, URL: link
• [MA] Janosch Baltensperger: A Secure Aggregation Protocol for Decentralized Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, August 2023, URL: link
• [BA] Florian Andreas Herzog: Fully Fledged SDN in a LoRa Mesh; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, September 2023, URL: link
• [BA] Gregory Frommelt: Linux on Tensilica Xtensa; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, September 2023, URL: link
• [MA] Lynn Zumtaugwald: Designing and Implementing an Advanced Algorithm to Measure the Trustworthiness Level of Federated Learning Models; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, August 2023, URL: link
• [MA] Róbert Oles: Detection and Classification of Malware using File System Dimensions for MTD on IoT; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, May 2023, URL: link
• [MA] Jan Kreischer: Federated Reinforcement Learning for Private and Collaborative Selection of Moving Target Defense Mechanisms for IoT Device Security; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, June 2023, URL: link
• [MA] Chenfei Ma: Design and Prototypical Implementation of the Node Selection Strategy in Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, October 2023, URL: link
• [MAP] Han, Jing and Cheng, Xi and Zeng, Zien and Ren, Heqing: Creation of New Datasets for Decentralized Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, Jan. 2024, URL: link
• [MA] Ye Zi: Mitigating Poisoning Attacks in Decentralized Federated Learning through Moving Target Defense; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, Mar. 2024, URL: link  
• [IS] Yuanzhe Gao: Design and Implementation of a Privacy Auditing Component for the Decentralized Federated Learning Framework; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, Jun. 2024, URL: link  

• [MA] Xiao Chen: Design and Implementation of an Information Metrics-based Anomaly Model Detector in Decentralized
  Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, Jul. 2024, URL: link 

• [MA] Hongjie Guan: FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, Jul. 2024, URL: link 

• [MA] Xi Cheng: Design and Implementation of Environmental Sustainability Module for Decentralized Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, August 2024, URL: link

• [MA] Wenzhe Li: Design and Implementation of a Black-box Robustness Analysis Module for an DFL Platform; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, August 2024, URL: link
• [MA] Zien Zeng: Improving the Model Robustness of DFL in Non-IID Environment; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, August 2024, URL: link
• [MAP] Runxi Cui, Yunlong Li: Novel Poisoning Attacks on Decentralized Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, August 2024, URL: link
• [IS] Lucas Krauter: Data Exploration and Feature Engineering for an IoT Device Behavior Fingerprinting Dataset; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, August 2024, URL: link
• [MA] Yuanzhe Gao: Novel Topology Inference Attacks on DFL; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, October 2024, URL: link
• [MA] Nicolas Kohler: A Solution for Decentralized Federated Multi-Task Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, October 2024, URL: link
• [MAP] Timothy-Till Näscher, Witold Rozek: Feature Integration for an Open-source Decentralized Federated Learning Platform; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, January 2025, URL: link.
• [MAP] Nazek Olabi, Michael Vuong: Dimensionality Reduction for Distributed Machine Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, April 2025, URL:link
• [BA] Nicolas Huber: Deploying a Decentralized Federated Learning Platform in Resource-Constrained Devices; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, April 2025, URL: link
• [MA] Heqing Ren: Design and Implementation of an MTD-based Mitigation Approach for Membership Inference Attacks; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, April 2025, URL: link