Thesis (Ph.D.) - Indiana University, Informatics, Computing, and Engineering/University Graduate School, 2019
Detection of anomalous events relies on the collection, filtration, and analysis of diverse types of temporal data. Interactions derived from such data can be modeled as networks to provide a better understanding of the structure and dynamics of the underlying systems. This dissertation examines the temporal evolution of Internet-scale phenomena to provide a more nuanced characterization of normal functionality and anomalous behavior—usually undesired and often malicious. Characterizing regular behavior is often a prerequisite for identifying these anomalies. However, the volume and patterns of interactions during a system’s evolution under particular circumstances may be highly variant. In security, I create hypotheses about the nature of attacks as a core component of detection. In the insider threat, I hypothesized that malicious insiders would require access to identifiably more diverse repositories than the non-malicious. In routing, my first hypothesis was that the role of nation-state actors could be identified using traditional macroeconomic analyses. My second hypothesis, that control plane hijacking could be identified by leveraging k-shell decomposition of Autonomous System level graphs, could not be validated. In contrast, the analysis of inter-arrival times of route announcements provided clear identification and early warning of large-scale incidents. This dissertation contributes to a more comprehensive understanding of security threats using data and network science methods. Specifically, I use (i) Graph mining to show that surprising patterns about community structure and k-shell decomposition of graphs can be leveraged to detect classes of anomalies. Leveraging (ii) Graph robustness, I show how community detection-based methods are less biased against the density of edges in the system, providing a robust approach to detect anomalous behavior. Finally, I illustrate the potential of (iii) Graph anomaly detection for identifying anomalies in different real-world scenarios, including (a) email interactions, (b) social media, (c) code repositories, and (d) Internet control-plane updates.