{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/42892"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/42892","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"A Cybersecurity Framework Using Machine Learning for Red Team Operators","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. 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