Bayesian Discovery of Threat Networks

Autor: Kenneth D. Senne, Scott Philips, Garrett Bernstein, Edward K. Kao, Steven T. Smith
Jazyk: angličtina
Rok vydání: 2013
Předmět:
Popis: A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a diffusion model for threat. A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utilizes spatio-temporal relationships between vertices, and is used for a specific space-time formulation that leads to significant performance improvements on coordinated covert networks. This performance is demonstrated using a new hybrid mixed-membership blockmodel introduced to simulate random covert networks with realistic properties.
IEEE Trans. Signal Process., major revision of arxiv.org/abs/1303.5613. arXiv admin note: substantial text overlap with arXiv:1303.5613
Databáze: OpenAIRE