// Workers AI · dad joke modeWhat did the prodigal son's wallet say? I'm spent.
 | This article needs to be updated. (October 2021) |
| Establishment | 2011 |
|---|---|
| Sponsor | DARPA |
| Value | $9 million |
| Goal | Rapidly data mine large sets to discover anomalies |
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]
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]
Primary participants
[edit]See also
[edit]References
[edit]- 1 2 3 4 "Video Interview: DARPA's ADAMS Project Taps Big Data to Find the Breaking Bad". Inside HPC. November 29, 2011. Retrieved December 5, 2011.
- ↑ Brandon, John (December 3, 2011). "Could the U.S. Government Start Reading Your Emails?". Fox News. Archived from the original on December 3, 2011. Retrieved December 6, 2011.
- 1 2 3 "Georgia Tech Helps to Develop System That Will Detect Insider Threats from Massive Data Sets". Georgia Institute of Technology. November 10, 2011. Retrieved December 6, 2011.
- ↑ Storm, Darlene (December 6, 2011). "Sifting through petabytes: PRODIGAL monitoring for lone wolf insider threats". Computer World. Archived from the original on January 12, 2012. Retrieved December 6, 2011.
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