Date of Award
8-2016
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Jennifer Neville
Committee Chair
Jennifer Neville
Committee Member 1
Daniel Aliaga
Committee Member 2
Chris Clifton
Committee Member 3
David Gleich
Abstract
An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they could be examples of malicious activity or network malfunction. Hypothesis testing using network statistics to summarize the behavior of the network provides a robust framework for the anomaly detection decision process. Unfortunately, choosing network statistics that are dependent on confounding factors like the total number of nodes or edges can lead to incorrect conclusions (e.g., false positives and false negatives). In this dissertation we describe the challenges that face anomaly detection in dynamic network streams regarding confounding factors. We also provide two solutions to avoiding error due to confounding factors: the first is a randomization testing method that controls for confounding factors, and the second is a set of size-consistent network statistics which avoid confounding due to the most common factors, edge count and node count.
Recommended Citation
Fond, Timothy La, "Controlling for confounding network properties in hypothesis testing and anomaly detection" (2016). Open Access Dissertations. 791.
https://docs.lib.purdue.edu/open_access_dissertations/791