Hi, I'm a Ph.D. student in the computer science department of Tulane University. My research interests are:
Reinforcement Learning, IoT and Game Theory.
Welcome to my webpage!
08/2018 - 06/2021
AI for security
SaTC: CORE: Small: Towards Robust Moving Target Defense: A Game Theoretic and Learning Approach
The proposed research contributes to the emerging field of the science of security via a cross-disciplinary approach that combines techniques from cybersecurity, game theory, and machine learning.
08/2019 - 06/2020
AI for society
Dynamic Mechanisms for a Safer and More Flexible Ride-Sharing System
In this project, we will combine the classic economics models with model-free learning approaches to design more practical and explainable dynamic mechanisms for ride-sharing systems.
AI for business
09/2011 - 06/2015
Xi’an Jiaotong-Liverpool University
BSc Applied Mathematics
09/2015 - 12/2016
University of Liverpool
MSc Computer Science
03/2018 - present
Ph.D. Computer Science
Henger Li, Zizhan Zheng
Optimal Timing of Moving Target Defense: A Stackelberg Game Model. Military Communications Conference (MILCOM) 2019
As an effective approach to thwarting advanced attacks, moving target defense (MTD) has been applied to various domains. Previous works on MTD, however, mainly focus on deciding the sequence of system configurations to be used and have largely ignored the equally important timing problem. Given that both the migration cost and attack time vary over system configurations, it is crucial to jointly optimize the spatial and temporal decisions in MTD to better protect the system from persistent threats. In this work, we propose a Stackelberg game model for MTD where the defender commits to a joint migration and timing strategy to cope with configuration-dependent migration cost and attack time distribution. The defender's problem is formulated as a semi-Markovian decision process and a nearly optimal MTD strategy is derived by exploiting the unique structure of the game.