Year
2018
Abstract
The Fukushima Daiichi nuclear power plant accident in 2011 raised a fear that terrorists could cause a similar accident by acts of sabotage against nuclear power plant and it is noticeable that threats of terrorism for nuclear security are increased after the accident. When considering sabotage, the prime threat to nuclear power plants, due attention should be paid to sabotage by insiders. They could take advantage of their access authority and knowledge, to bypass dedicated physical protection elements or other provisions [1].The primary physical protection system functions are deterrence, detection, delay and response. It is noticeable that if detection failed, delay and response would become invalid. Thus, detection of insiders' sabotage should be enhanced. Considering current countermeasures of physical protection system to insiders' sabotage, the most significant challenge is how to distinguish ordinary maintenance behaviors and malicious behaviors since some malicious behaviors may hidden in ordinary maintenance behaviors.It appears that hand behavior has high contribution to human activity and a significant portion of maintenance behaviors and malicious behaviors. In this study, we proposed a hand behavior detection algorithm for insiders' malicious behaviors for nuclear security [2]. Meanwhile, a real-time fingertip tracking system was developed and time-series data of each fingertip was successfully obtained. Moreover, a time-series data analysis based behavior recognition method was developed and all assumed malicious behaviors were detected with high accuracy and high speed. Also, robustness of the behavior recognition method was verified.