Research Scientist
Siemens Corporate Technology, Beijing
Bio
I am currently a research scientist at Siemens Corporate Technology, researching on industrial data analytics and artificial intelligence.
Before that, I was a research associate at the Institute for Security Science&Technology(ISST), Imperial College London.
I obtained my PhD in Quantitative Modelling with Professor Jane Hillston at the Laboratory for Foundations of Computer Science , School of Informatics, University of Edinburgh.
My research interests are in the areas of performance modelling and cyber security. Over the past five years, I have been focusing on creating and applying quantitative modelling and machine learning techniques to help design and build more intelligent, secure and dependable systems for smart city applications and critical national infrastructures.
Research interests: Big data driven cyber security; Deep learning application in industrial control systems; System performance and dependability analysis; Large-scale stochastic models.
Research Experience
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Present 2018.3
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2018.2 2016.9
Postdoctoral Researcher
Imperial College London, Institute for Security Science and Technology
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2017.7 2017.6
Visiting Fellow
Singapore University of Technology and Design, iTrust Center for Research in Cyber Security
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2016.9 2013.6
Research Assistant
University of Edinburgh, Laboratory for Foundations of Computer Science
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2014.5 2014.3
Visiting Researcher
Politecnico di Milano, Performance Evaluation Group
Education Background
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Ph.D. 2016
Ph.D. in Computer Science -- Quantitative Modelling
University of Edinburgh
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M.Sc.2012
Master of Computer Science -- High Performance Computing
University of Edinburgh
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B.Eng.2011
Bachelor of Engineering -- Computer Science and Technology
Beijing Language and Culture University
Projects
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2016-PresentSecurity by Design of Interconnected Critical Infrastructures (UK EPSRC Funded)
The project is aimed to advance the design of secure interconnected public infrastructures with focus on Security-by-Design. The project is focusing on security analysis through modelling, based on the abstraction of system design, impact and response analysis across interconnected infrastructures, and the upgrading of initial design to improve system resilience to cyber-attacks. -
2013-2016QUANTICOL (EU FP7 Funded)
With smart city applications in mind, the main objective of the QUANTICOL project is the development of an innovative formal design framework that provides a specification language for collective adaptive systems (CAS) and a large variety of tool-supported, scalable analysis and verification techniques. These techniques will be based on the original combination of recent breakthroughs in stochastic process algebras and associated verification techniques, and mean field/continuous approximation and control theory. Such a design framework will provide scalable extensive support for the verification of developed models, and also enable and facilitate experimentation and discovery of new design patterns for emergent behaviour and control over spatially distributed CAS.
Publications
A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems
Working Paper arXiv:1709.06397
Accelerating Simulation of Population Continuous Time Markov Chains via Automatic Model Reduction
Journal Paper Performance Evaluation
Moment-based Availablity Prediction for Bike-sharing Systems
Journal Paper Performance Evaluation
Multi-level Anomaly Detection of Industrial Control Systems via Package Signatures and LSTM Networks
Conference Paper 47th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2017)
Moment-based Probabilistic Prediction of Bike Availability for Bike-sharing Systems
Conference Paper 13th international conference on Quantitative Evaluation of Systems (QEST 2016)
Availability Modeling of Generalized k-out-of-n: G Warm Standby Systems With PEPA
Journal Paper IEEE Transactions on Systems, Man, and Cybernetics: Systems.
Location Aggregation of Spatial Population CTMC Models
Workshop Paper 14th international workshop on Quantitative Aspects on Programming Languages and Systems (QAPL 2016)
Automatic Moment-closure Approximation of Spatially Distributed Collective Adaptive Systems
Journal Paper ACM Transactions on Modelling and Computer Simulation
Performance Analysis of Collective Adaptive Behaviour in Time and Space
Journal Paper Electronic Notes in Theoretical Computer Science
Speed-Up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction
Workshop Paper 12th European Workshop on Computer Performance Engineering (EPEW 2015)
PALOMA: A Process Algebra for Located Markovian Agents
Conference Paper 11th International Conference on Quantitative Evaluation of Systems (QEST 2014)
Patch-based Hybrid Modelling of Spatially Distributed Systems by Using Stochastic HYPE - ZebraNet as an Example
Workshop Paper 12th international workshop on Quantitative Aspects on Programming Languages and Systems (QAPL 2014)
Teaching
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2016 2015
Object Oriented Programming
Tutor, undergraduate course in school of Informatics, University of Edinburgh
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2015 2014
Introduction to Java Programming
Tutor, undergraduate course in school of Informatics, University of Edinburgh
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2014 2013
Performance Modelling
Teaching Assistant, master course in school of Informatics, University of Edinburgh
Miscellaneous
I serve as a journal reviewer for IEEE Transactions on Reliability and IEEE Transactions on Intelligent Transportation Systems.
I was a reviewer of AAMAS 2018, and a PC member of DataMod 2017, ALP4IoT 2017.
My Github address is link.
Contact me: c.feng #at# imperial.ac.uk