About: Anomaly Detection at Multiple Scales   Sponge Permalink

An Entity of Type : dbkwik:resource/xoykDFxJFBgF02W_HRnEzw==, within Data Space : 134.155.108.49:8890 associated with source dataset(s)

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.

AttributesValues
rdf:type
rdfs:label
  • Anomaly Detection at Multiple Scales
rdfs:comment
  • 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.
sameAs
dcterms:subject
dbkwik:military/pr...iPageUsesTemplate
Label
  • Website
  • Value
  • Sponsor
  • Goal
  • Establishment
Title
  • Anomaly Detection at Multiple Scales
Data
abstract
  • 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.
Alternative Linked Data Views: ODE     Raw Data in: CXML | CSV | RDF ( N-Triples N3/Turtle JSON XML ) | OData ( Atom JSON ) | Microdata ( JSON HTML) | JSON-LD    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3217, on Linux (x86_64-pc-linux-gnu), Standard Edition
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2012 OpenLink Software