Probabilistic Performance Analysis of Moving Target and Deception Reconnaissance Defenses

Abstract

Deception and moving target reconnaissance defenses are techniques that attempt to invalidate information an attacker attempts to gather. Deception defenses attempt to mislead attackers performing network reconnaissance, while moving target defenses seek to make it more difficult for the attacker to predict the state of their target by dynamically altering what the attacker sees. Although the deployment of reconnaissance defenses can be effective, there are nontrivial administration costs associated with their configuration and maintenance. As a result, understanding under the circumstances these defenses are effective and efficient is important. This paper introduces probabilistic models for reconnaissance defenses to provide deeper understanding of the theoretical effect these strategies and their parameters have for cyber defense. The models quantify the success of attackers under various conditions, such as network size, deployment of size, and number of vulnerable computers. This paper provides a probabilistic interpretation for the performance of honeypots, for deception, and network address shuffling, for moving target, and their effect in concert. The models indicate that a relatively small number of deployed honeypots can provide an effective defense strategy, often better than movement alone. Furthermore, the models confirm the intuition that that combining, or layering, defense mechanisms provide the largest impact to attacker success while providing a quantitative analysis of the improvement and parameters of each strategy.