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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.〔(【引用サイトリンク】title=ADAMS )〕 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 government employee 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.〔(【引用サイトリンク】title=Anomaly Detection at Multiple Scales )〕 == See also == * Cyber Insider Threat * Einstein (US-CERT program) * Threat (computer) * Intrusion detection 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Anomaly Detection at Multiple Scales」の詳細全文を読む スポンサード リンク
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