THIS IS A PAST EVENT.
A Masters Defense presented by Alina Pacheco-Rodriguez.
As the size and diversity of computer networks increases, management becomes a more tedious and challenging task for network administrators. Interconnected devices require different types of access to maintain functionality; however, access must also be constrained to ensure security. Virtual Local Area Networks (VLANs) has become a core technology for securely interconnecting devices in a computer network since it can associate devices with various groups, where each group can have different access rights. Although VLANs offer the flexibility of different groups and various access rights, finding the smallest, most manageable number of VLANs to securely provide necessary interconnections is difficult.
This thesis investigates the use of Genetic Algorithms (GAs) to discover the minimum number of VLANs, and the associated access rights, necessary for connectivity and security in a computer network. Using this approach, VLAN configurations are modeled as chromosomes and a series of selection, crossover, and mutation operations are performed to find suitable VLAN configurations. Since the problem is a multi-objective search, a Pareto-based fitness measure was developed to rank possible VLAN solutions, where better solutions have fewer VLANs and higher security. Simulation was used to compare the proposed GA-based approach with other search methods, such as Beam search. Experimental results indicate this approach is able to consistently find concise and secure VLAN groups under various conditions, including increasing number of interconnected devices and more restrictive number of required VLANs.
Monday, April 22, 2019 at 12:00pm
Manchester Hall, 017
3430 McPherson Rd. Winston-Salem, NC 27109
Lesley Whitener
3367584982
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