Proactive Discovery of Insider Threats Using Graph Analysis and Learning

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Proactive Discovery of Insider Threats Using Graph Analysis and Learning
Establishment 2011
Sponsor DARPA
Value $9 million
Goal Rapidly data mine large sets to discover anomalies

Proactive Discovery of Insider Threats Using Graph Analysis and Learning or PRODIGAL is a computer system for predicting anomalous behavior amongst humans by data mining network traffic such as emails, text messages and log entries.[1] It is part of DARPA's Anomaly Detection at Multiple Scales (ADAMS) project.[2] The initial schedule is for two years and the budget $9 million.[3]

It uses graph theory, machine learning, statistical anomaly detection, and high-performance computing to scan larger sets of data more quickly than in past systems. The amount of data analyzed is in the range of terabytes per day.[3] The targets of the analysis are employees within the government or defense contracting organizations; specific examples of behavior the system is intended to detect include the actions of Nidal Malik Hasan and Wikileaks alleged source Bradley Manning.[1] Commercial applications may include finance.[1] The results of the analysis, the five most serious threats per day, go to agents, analysts, and operators working in counterintelligence.[1][3][4]

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