Design Of Intrusion Detection System Using Background Machine Learning

  • Kim, Hyung-Hoon (Dept. of Cosmetic Science, Kwangju Womens University) ;
  • Cho, Jeong-Ran (Dept. of Health Administration, Kwangju Womens University)
  • Received : 2019.04.25
  • Accepted : 2019.05.20
  • Published : 2019.05.31


The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.


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Fig. 1. Overview of foreground motion detection and extracting algorithm

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Fig. 2. Structure of artificial neural network

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Fig. 3. Overview of Background Learning Intrusion Detection System

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Fig. 4. image processing and subtract image based intrusion detection

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Fig. 5. Background neural network testing algorithm

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Fig. 6. Background neural network learning algorithm

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Fig. 7. Background artificial neural network concept


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