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This paper studies the feasibility of synthetic data generation formission-critical applications. The emphasis is on synthetic data generation foranomalous detection in complex social networks. In particular, the developmentof a heuristic generative model, capable of creating data for anomalous rareactivities in complex social networks is sought. To this end, lessons fromsocial and political literature are applied to prototype a novel implementationof the Agent-based Modeling (ABM) framework, based on simple socialinteractions between agents, for synthetic data generation in the context ofterrorist profile desegregation. The conclusion offers directions for furtherverification, fine-tuning, and proposes future directions of work for the ABMprototype, as a complex-societal approach to synthetic data generation, byidentifying heuristic hyper-parameter tuning methodologies to further ensurethe generated data distribution is similar to the true distribution of theoriginal data-sets. While a rigorous mathematical optimization for reducing thedistances in distributions is not offered in this work, we opine that thisprototype of an autonomous-agent generative complex social model is useful forstudying and researching on pattern of life and anomaly detection where thereis strict limitation or lack of sufficient data for using practical machinelearning solutions in mission-critical applications.

On synthetic data generation for anomaly detection in complex social networks