The goal of the Modeling Adversarial Activity (MAA) program is to develop mathematical and computational techniques for modeling adversarial activity for the purpose of producing high-confidence indications and warnings of efforts to acquire, fabricate, proliferate, and/or deploy weapons of mass terror (WMTs). MAA assumes that an adversary’s WMT activities will result in observable transactions. While the probability that any one source alone will reveal a WMT threat may be low, the probability of detecting a WMT threat can be increased by appropriately integrating multiple sources of transaction data.
MAA requires synthetic transaction data to drive the development of techniques and tools in ways that will avoid the privacy and classification issues that can be associated with real-world data. MAA will develop the means to create synthetic transaction data sets that are both realistic and fully releasable to the scientific community, i.e., data that contains neither personally identifiable information nor restrictions with respect to classification. Because transaction data may very naturally be modelled using graphs, mathematical and computational methods to enable large-scale graph analytics including graph alignment and merging, sub-graph detection, and sub-graph matching are of particular interest.
Additional details are available at DARPA-BAA-16-61: Modeling Adversarial Activity.
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