Towards a Theory of Moving Target Defense

Abstract

The static nature of cyber systems gives attackers the advantage of time. Fortunately, a new approach, called the Moving Target Defense (MTD) has emerged as a potential solution to this problem. While promising, there is currently little research to show that MTD systems can work effectively in real systems. In fact, there is no standard definition of what an MTD is, what is meant by attack surface, or metrics to define the effectiveness of such systems. In this paper, we propose an initial theory that will begin to answer some of those questions. The paper defines the key concepts required to formally talk about MTD systems and their basic properties. It also discusses three essential problems of MTD systems, which include the MTD Problem (or how to select the next system configuration), the Adaptation Selection Problem, and the Timing Problem. We then formalize the MTD Entropy Hypothesis, which states that the greater the entropy of the system's configuration, the more effective the MTD system.

Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

“…In addition, the authors enhanced their GAbased MTD to change computer configurations by changing mutation as well as using the feedback about system security status [84]. Zhuang et al [172] also used a GA to generate system configurations with high diversity for maximizing system security.…”

CHAOS: An SDN-Based Moving Target Defense System

“…Their simulation results show the potential for MTD to be effective in preventing attacks against computer networks. Furthermore, they proposed a formal theory to describe the MTD system and its basic properties and formalized the MTD entropy hypothesis, which states that the greater the entropy of the system configuration, the more effective the MTD system [22,23]. Stallings proposed the use of SDN in the implementation of MTD mitigations.…”

Markov Modeling of Moving Target Defense Games

“…Zhuang [33,36] made a first attempt using Markov models to analyze MTD strategies; each state in the proposed Markov model represents the complete configuration of the whole system which means that the number of states in the Markov model grows exponentially in the number of machines in the system. This attempt being too complex, Zhuang [33,35] considers an attack graph as a Markov model where transitions are labeled with a probability density and where nodes keep track of the machine's configuration together with necessary overall system parameters of which the probability densities are a function.…”