Controlling for confounding network properties in hypothesis testing and anomaly detection

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Year

2016

Author dc:creator

• Fond, Timothy La

Contributors dc:contributor

• Jennifer Neville
• Daniel Aliaga
• Chris Clifton
• David Gleich

• Pure sciences
• Applied sciences
• Anomaly detection
• Data mining
• Dynamic graphs
• Computer Sciences
• Statistics and Probability

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/791

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1983