PRODIGAL
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PRODIGAL (proactive discovery of insider threats using graph analysis and learning) is a computer system for predicting anomalous behavior among humans, by data mining network traffic such as emails, text messages and server 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]
 | This article needs to be updated. (October 2021) |
| Establishment | 2011 |
|---|---|
| Sponsor | DARPA |
| Value | $9 million |
| Goal | Rapidly data mine large sets to discover anomalies |
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 source Chelsea 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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References
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