A Cybersecurity Framework Using Machine Learning for Red Team Operators
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.
Networked Digital Library of Theses and Dissertations