Proactive Discovery of Insider Threats

Proactive Discovery of Insider Threats

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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. It is part of DARPA's Anomaly Detection at Multiple Scales project. The initial schedule is for two years and the budget $9 million. 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. 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.
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Product details

  • Paperback | 156 pages
  • 152 x 229 x 9mm | 236g
  • United States
  • English
  • black & white illustrations
  • 6136275708
  • 9786136275703