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Nov 1 - 4, 2016
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Vol.9 No.2previous AASP17 (125-126-127-128-129-130-131-132-133-134-135-136-137-138-139-140-141-142) NT85

Academic Articles
Regular Paper Vol.9 No.2 (2017) p.66 - p.71
 

Insider Malicious Behaviors Detection and Prediction Technology for Nuclear Security

 
Shi CHEN 1,*, Kazuyuki DEMACHI 1, Tomoyuki FUJITA 1, Yutaro NAKASHIMA 1, and Yusuke KAWASAKI 1
 

1The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

 
Abstract
After Fukushima Daiichi nuclear power plant accident, the importance of nuclear security is increased, especially as a threat to nuclear power plants, sabotage by insider is significant. In response to the increasing threats to Nuclear Power Plant, human malicious behavior detection is necessary for nuclear security. Hand motion is an important part of human activity and has a high contribution for high-accuracy detection of insider malicious behaviors. Hand motions can be distinguished by the position of each fingertip, and both stretched and bend fingers of both left and right hands can be classified as different parts by using depth data and body index frame of Microsoft Kinect v2. Fingers were identified by using K-means clustering algorithm. Finally, it was built a hand motion time series data by using the developed real-time hand motion detection system. However, as malicious behaviors detection isn’t enough for nuclear security, future malicious behaviors prediction should be taken into consideration. In this research, the real-time hand motion detection system was developed by using Kinect v2. In addition, we explored the possibility of malicious behavior detection and prediction by using Stacked Auto-Encoder.
 
Keywords
Malicious Behavior Detection, Hand Motion Tracking, Kinect, Deep Neural Network, Stacked Auto-Encoder
 
Full Paper: PDF
Article Information
Article history:
Received 12 October 2016
Accepted 29 May 2017