It is well known that not all intrusions can be prevented and additional lines of defense are needed to deal with intruders. However, most current approaches use honey-nets relying on the assumption that simply attracting intruders into honeypots would thwart the attack. In this chapter, we propose a different and more realistic approach, which aims at delaying intrusions, so as to control the probability that an intruder will reach a certain goal within a specified amount of time. Our method relies on analyzing a graphical representation of the computer network’s logical layout and an associated probabilistic model of the adversary’s behavior. We then artificially modify this representation by adding "distraction clusters"-collections of interconnected virtual machines-at key points of the network in order to increase complexity for the intruders and delay the intrusion. We study this problem formally, showing it to be NP-hard and then provide an approximation algorithm that exhibits several useful properties. Finally, we compare recent approach for selecting a subset of distraction clusters with our prototypal implementation of the proposed framework and then unveil experimental results.