Bayesian Discovery of Threat Networks
| Autor: | Kenneth D. Senne, Scott Philips, Garrett Bernstein, Edward K. Kao, Steven T. Smith |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society Theoretical computer science Computer science FOS: Physical sciences Machine Learning (stat.ML) Network science Mathematics - Statistics Theory Statistics Theory (math.ST) Physics and Society (physics.soc-ph) computer.software_genre Machine Learning (cs.LG) Geometric networks Statistics - Machine Learning FOS: Mathematics Electrical and Electronic Engineering Connectivity Social and Information Networks (cs.SI) Wait-for graph Graph partition Bayesian network Computer Science - Social and Information Networks Graph Algebraic graph theory Computer Science - Learning Signal Processing Graph (abstract data type) Data mining computer |
| 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 |
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