Proposal of Insider Detection Method for Nuclear Security
出町 和之 Kazuyuki DEMACHI 川崎 祐典 Hironori KAWASAKI 陳 実 Shi CHEN 藤田 智之 Tomoyuki FUJITA 兼本 茂 Shigeru KANEMOTO
Convolution neural networkFeature extractionNuclear SecurityPrincipal Component AnalysisTime-series data analysis
Sabotage by malicious insider is one of significant and serious threats for nuclear security of nuclear power plants. It is difficult, however, to distinguish abnormal behaviors from normal works such as their daily maintenance activities. In this study, a technique was proposed to subdivide the abnormal behavior due to sabotage by image analysis and then to detect and identify the abnormal behavior in real time.