A Cybersecurity Framework Using Machine Learning for Red Team Operators
type: CreativeWork
description: As the use of online services increases, so does the risk of compromised personal data. In response, organizations deploy blue and red teams to protect their networks from cyber-attacks. While numerous solutions exist for supporting blue team efforts, there is a lack of equivalent support for red teams. This thesis proposes a real-time framework designed to assist red teams in their tasks, automating redundant tasks and suggesting potential network vulnerabilities. The framework also offers integration of machine learning algorithms for predicting probable attack paths. Moreover, we evaluate a Hidden Markov Model algorithm on a network and evaluate the performance of the framework.
abstract: As the use of online services increases, so does the risk of compromised personal data. In response, organizations deploy blue and red teams to protect their networks from cyber-attacks. While numerous solutions exist for supporting blue team efforts, there is a lack of equivalent support for red teams. This thesis proposes a real-time framework designed to assist red teams in their tasks, automating redundant tasks and suggesting potential network vulnerabilities. The framework also offers integration of machine learning algorithms for predicting probable attack paths. Moreover, we evaluate a Hidden Markov Model algorithm on a network and evaluate the performance of the framework.
author: Singh, Abhijeet
language: en
date published: 2023
date modified: 2026-07-24T01:34:27Z
provider: Carleton University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Carleton University

PropertyValue
property ID: Handle
value: https://hdl.handle.net/20.500.14718/42892

PropertyValue
property ID: DOI
value: 10.22215/etd/2023-15499