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.

Name thesis:degree_name

Master of Information Technology (M.I.T.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Digital Media

Grantor dc:publisher

Carleton University

Year dc:date.issued

2023

Author dc:creator

• Singh, Abhijeet

Statement dc:rights

• Copyright © 2023 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2023-15499

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/42892