Degree Name
MS (Master of Science)
Program
Computer Science
Date of Award
12-2024
Committee Chair or Co-Chairs
Mohammad Khan
Committee Members
Biju Bajracharya, Shehenaz Shaik
Abstract
Dynamic attributed graphs, which evolve over time and hold node-specific attributes, are essential in fields like social network analysis, where anomalous node detection is a growing area. Vehicular social networks (VSNs), a subset of these graphs, are ad hoc networks in which vehicles exchange data with one another and with infrastructure. In this dynamic context, identifying anomalous nodes is challenging but crucial for maintaining trust within the network. This work presents an unsupervised deep learning approach for anomalous node detection in VSNs. This model achieved an accuracy of 71% while detecting synthetic anomalies in a simulated network based on real-world data. This approach demonstrates the potential of unsupervised methods for reliable anomaly detection in scenarios where traditional classification proves difficult or impractical.
Document Type
Thesis - unrestricted
Recommended Citation
Hamilton, Austin, "Detecting Anomalies in Dynamic Attributed Graphs: An Unsupervised Learning Approach" (2024). Electronic Theses and Dissertations. Paper 4471. https://dc.etsu.edu/etd/4471
Copyright
Copyright by the authors.