Intruder Alert: Dimension Reduction and Density-Based Clustering for a Cybersecurity Application
Public Deposited- Resource Type
- Creator
- Abstract
This thesis examines the use of Principal Component Analysis, Robust Principal Component Analysis, and simple autoencoders for dimension reduction on a synthetic cybersecurity dataset. Each is tested as a precur- sor to Independent Component Analysis. Stable independent components are obtained by iterative random- ized starts to FastICA and selecting the centroids of the hierarchically clustered components. A density-based clustering method is then applied to the results with the goal of isolating malicious observations from benign ones using greatest distance between centroids as a heuristic metric of success. The method is then applied to a real-world cybersecurity dataset from an industry partner.
- Subject
- Language
- Publisher
- Thesis Degree Level
- Thesis Degree Name
- Thesis Degree Discipline
- Identifier
- Rights Notes
Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used 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.
- Date Created
- 2021
Relations
- In Collection:
Items
Thumbnail | Title | Date Uploaded | Visibility | Actions |
---|---|---|---|---|
burr-intruderalertdimensionreductionanddensitybased.pdf | 2023-05-05 | Public | Download |