[review#27] Survey of Cyber Moving Targets Second Edition_2018
This report is the result of studies performed at Lincoln Laboratory, a federally funded research and development center operated by Massachusetts Institute of Technology.
Paper review
[review#26] Moving Target Defense Meets Artificial-Intelligence-Driven Network: A Comprehensive Survey_2025
In this article, we first provide a thorough analysis of the threats in the cloud–edge–terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer.
[review#25] Game Theory Approaches for Evaluating the Deception-based Moving Target Defense_2022
In this paper, we propose a concept of deception attack surface to illustrate deception-based moving target defense. Moreover, we propose a quantitative method to measure deception, which includes two core concepts: exposed falseness degree and hidden truth de
gree.
[review#24] Low delay network attributes randomization to proactively mitigate reconnaissance attacks in industrial control systems_2023
This paper presents a proactive network reconnaissance defense mechanism based on the temporal randomization of network IP addresses, MAC addresses and port numbers.
[review#23] Moving Target Defense: creating asymmetric uncertainty for cyber threats
The chapters in this book present a range of MTD challenges and promising solution paths based on game–theoretic approaches, network-based cyber maneuver, and software transformations.
[review#22] Cost-effective moving target defense against DDoS attacks using trilateral game and multi-objective Markov decision processes_2020
By designing a trilateral game cost-effective shuffling algorithm, we capture the best MTD strategy and reach a balance between them in a given shuffling scenario.
[review#21] A survey: When moving target defense meets game theory_2023
This study presents the basic concepts of MTD and game theory, followed by a literature review, to study MTD decision-making methods based on game theory from the dimensions of space, time, space–time, and bounded rationality.
[review#20] Privacy Games_Chen, Or and Salil, 2020
Our work is aimed at better understanding the behavior of agents in settings where their privacy concerns are explicitly given. We consider a toy setting where agent A, in an attempt to discover the secret type of agent B, offers B a gift that one type of B agent likes and the other type dislikes.
[review#19] Truthful mechanisms for agents that value privacy_Chen et al, 2016
In this work, we propose a new, general way of modeling privacy in players’ utility functions. Specifically, we only assume that if an outcome o has the property that any report of player i would have led to o with approximately the same probability, then o has a small privacy cost to player i.
[review#18] Is Privacy Compatible with Truthfulness?_Xiao, 2013
In the area of privacy-preserving data mining, a differentially private mechanism intuitively encourages people to share their data because they are at little risk of revealing their own information.