Security Event Classification Method for Fiber-optic Perimeter Security System Based on Optimized Incremental Support Vector Machine

The way of efficiently classifying the fence climbing, fabric cutting, wall breaking and other environment factors, is an imperative problem for fiber-optic perimeter security system. To solve this problem, a security threats classification method based on optimized incremental support vector machine is proposed. In this method the artificial bee colony algorithm is introduced to optimize the penalty factor and kernel parameter of incremental support vector machine under specified fitness function, and the optimized incremental support vector machine is used to classify the perimeter security threats. To testify the performance of the proposed method, the experiment based on UCI datasets and actual vibration signal are made. Comparing with the support vector machine optimized by other algorithms, higher classification accuracy and less time consumption is achieved by the proposed method. Therefore, the effectiveness and the engineering application value of this proposed method is testified.

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