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Generating Test Data for Insider Threat Detectors

The threat of malicious insider activity continues to be of paramount concern in both the public and private sectors. Though there is great interest in advancing the state of the art in predicting and stopping these threats, the difficulty of obtaining suitable data for research, development, and testing remains a significant hindrance. We outline the use of a synthetic data generator to enable research progress, while discussing the benefits and limitations of synthetic insider threat data, the meaning of realism in this context, comparisons to a hybrid real/synthetic data approach, and future research directions.