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Anomaly Detection at Multiple Scales

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Anomaly Detection at Multiple Scales, or ADAMS, is a $35 million DARPA project designed to identify patterns and anomalies in very large data sets. It is under DARPA's Information Innovation office and began in 2011. The Proactive Discovery of Insider Threats Using Graph Analysis and Learning is part of the ADAMS project. The Georgia Tech team includes noted high-performance computing researcher David A. Bader.

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Sponsor Value Goal Establishment Website

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Anomaly Detection at Multiple Scales

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Detect insider threats in defense and government networks 3.5E7 2011 n15: n18:

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Anomaly Detection at Multiple Scales, or ADAMS, is a $35 million DARPA project designed to identify patterns and anomalies in very large data sets. It is under DARPA's Information Innovation office and began in 2011. The project is intended to detect and prevent insider threats such as "a soldier in good mental health becoming homicidal or suicidal", an "innocent insider becoming malicious", or a "a government employee [whom] abuses access privileges to share classified information". Specific cases mentioned are Nidal Malik Hasan and Wikileaks alleged source Bradley Manning. Commercial applications may include finance. The intended recipients of the system output are operators in the counterintelligence agencies. The Proactive Discovery of Insider Threats Using Graph Analysis and Learning is part of the ADAMS project. The Georgia Tech team includes noted high-performance computing researcher David A. Bader.