Anomaly Intrusion Detection System Using Gaussian Mixture Model

Gaussian Mixture Model is a popular method to detect moving targets for static cameras. Since the traditional Gaussian Mixture Model has a poor adaptability when the illumination is changing in the scene and has passive learning rate, this paper describes a method that can detect illumination variation and update the learning rate adaptively. It proposes an approach which uses the color histogram matching algorithm and adjusts the learning rate automatically after introducing illumination variation factor and model parameters. Furthermore, the proposed method can select the number of describing model component adaptively, so this method reduced the computation complexity and improved the real-time performance. The experiment results indicate that the detection system gets better robustness, adaptability and stability.

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Automated defect detection system using wavelet packet frame and Gaussian mixture model

Journal of the Optical Society of America A ◽

10.1364/josaa.23.002690 ◽

2006 ◽

Vol 23 (11) ◽

pp. 2690 ◽

Cited By ~ 11

Author(s):

Soo Chang Kim ◽

Tae Jin Kang

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Defect Detection ◽

Detection System ◽

Wavelet Packet ◽

Gaussian Mixture

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Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function

Procedia Computer Science ◽

10.1016/j.procs.2015.03.174 ◽

2015 ◽

Vol 45 ◽

pp. 428-435 ◽

Cited By ~ 52

Author(s):

Ujwala Ravale ◽

Nilesh Marathe ◽

Puja Padiya

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Kernel Function ◽

Intrusion Detection System ◽

Detection System ◽

Rbf Kernel ◽

Anomaly Intrusion Detection ◽

Rbf Kernel Function ◽

Hybrid Anomaly

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An Embedded System Used as Intelligent Node of Distributed Surveillance

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.763 ◽

2013 ◽

Vol 475-476 ◽

pp. 763-766

Author(s):

Biao Yang ◽

Guo Yu Lin ◽

Wei Gong Zhang

Keyword(s):

Intrusion Detection ◽

Embedded System ◽

Intrusion Detection System ◽

Detection System ◽

Monitoring Network ◽

Abstract Object ◽

Intelligent Surveillance ◽

Intelligent Node ◽

Detection And Tracking ◽

Anomaly Intrusion Detection

The demand for intelligent surveillance is grown with the popularization of the camera monitoring network. An embedded system used as intelligent node of distributed surveillance is designed in this paper to improve the intelligent level of camera monitoring. Target detection and tracking can be implemented in this node and an anomaly intrusion detection system is designed based on the tracking results. Information sharing is realized via transmitting highly abstract object descriptors via the wireless network. A simple camera monitoring network is built in the labs to test the designed intelligent node and the experimental results indicate its effectiveness and accuracy.

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