Intruder Alert: Dimension Reduction and Density-Based Clustering for a Cybersecurity Application

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Creator: 

Burr, Benjamin

Date: 

2021

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: 

Mathematics
Statistics

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Science: 
M.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Statistics

Parent Collection: 

Theses and Dissertations

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