[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.
Moving Target Defense
Hidden Markov Models
[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#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.
Hidden Markov Models & three key Problems and Algorithms
This is a compilation of a series of videos in which Prof. Patterson describes the Hidden Markov Model, starting with the Markov Model and proceeding to the three key questions for HMMs. A Hidden Markov Model is a machine learning model for predicting sequences of states from indirect observations.