Explaining Automatically Designed Software Defined Perimeters with a Two Phase Evolutionary Computation System

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{frazier:2024:GECCOcomp,

• author = "James Gunder Frazier and Thomas Helmuth",
• title = "Explaining Automatically Designed Software Defined Perimeters with a Two Phase Evolutionary Computation System",
• booktitle = "Evolutionary Computation and Decision Making",
• year = "2024",
• editor = "Tinkle Chugh and Richard Allmendinger",
• pages = "1527--1535",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, evolutionary computation, decision making, cybersecurity, networks",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3664155",
• size = "9 pages",
• abstract = "Software Defined Perimeter (SDP) is a zero-trust network-isolation defense technique which aims to limit security risks by giving dynamic account type assignments to network users. Despite SDP being proven as an effective defense strategy in various domains, it has yet to see wide-spread use due to its drawbacks. One of SDP's most pressing issues is the need for an expert to manually configure it for each unique application. Here we describe a novel system for designing SDP networks called SDPush which can automatically design and analyze possible configurations for a given network with user-specifications. Since there is not a systematic approach for account type design and assignment, we develop a two-step optimization system consisting of a bitstring genetic algorithm and a genetic programming sub-system for designing and evaluating SDP networks respectively. In order to evolve an SDP configuration exhibiting the user-specified characteristics while also minimizing security risk, we implement our system to support multi-objective search spaces by providing the system's training set with different cases aimed at evaluating different aspects of the network configuration. We present initial results of experiments on networks of varying size and characteristic requirements.",
• notes = "GECCO-2024 EC+DM A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for James Gunder Frazier Thomas Helmuth

Citations