ANN-based Multi Classifier for Identification of Perimeter Events

Identification of perimeter events enables smarter perimeter security systems. This paper presents a multi classifier. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the bottom to build the classifier. The top level employs voting mechanism to identify intrusions, taking time e...

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Bibliographic Details
Main Authors: Hu Yan, Lixin Li, Fangchun Di, Jin Hua, Qiqiang Sun
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural network
Artificial neural networks
Feature extraction
Intrusion detection
Optical fiber sensors
Perimeter Intrusion detection
Reliability
Smart
Support vector machine
Support vector machines
Vibration signal
Vibrations
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Summary:Identification of perimeter events enables smarter perimeter security systems. This paper presents a multi classifier. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the bottom to build the classifier. The top level employs voting mechanism to identify intrusions, taking time evolution characters into account. In addition, to make the classifier be more self-adaptive, an incremental learning module is introduced. The proposed classifier has been successfully applied to oil and gas pipeline intrusion detection systems. Practical results show that it can distinguish nuisance events from intrusion events at a high rate of 94.86% and for seven kinds of intrusions, the recognition rate is 95.29%, fully satisfies the real application requirement.
DOI:10.1109/ISCID.2011.141