Address shuffling is a type of moving target defense that prevents an attacker from reliably contacting a system by periodically remapping network addresses. Although limited testing has demonstrated it to be effective, little research has been conducted to examine the theoretical limits of address shuffling. As a result, it is difficult to understand how effective shuffling is and under what circumstances it is a viable moving target defense. This paper introduces probabilistic models that can provide insight into the performance of address shuffling. These models quantify the probability of attacker success in terms of network size, quantity of addresses scanned, quantity of vulnerable systems, and the frequency of shuffling. Theoretical analysis shows that shuffling is an acceptable defense if there is a small population of vulnerable systems within a large network address space, however shuffling has a cost for legitimate users. These results will also be shown empirically using simulation and actual traffic traces.

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The difficulty of securing computer infrastructures increases as they grow in size and complexity. Network-based security solutions such as IDS and firewalls cannot scale because of exponentially increasing computational costs inherent in de tecting the rapidly growing number of threat signatures. Host based solutions like virus scanners and IDS suffer similar issues that are compounded when enterprises try to monitor them in a centralized manner. Swarm-based autonomous agent systems like digital ants and artificial immune systems can provide a scalable security solution for large network environments. The digital ants approach offers a biologically inspired design where each ant in the virtual colony can detect atoms of evidence that may help identify a possible threat. By assembling the atomic evidences from different ant types the colony may detect the threat. This decentralized approach can require, on average, fewer computational resources than traditional centralized solutions; however there are limits to its scalability. This paper describes how dividing a large infrastructure into smaller, managed enclaves allows the digital ant framework to effectively operate in larger environments.Experimental results will show that using smaller enclaves allows for more consistent distribution of agents and results in faster response times.