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Fully modelling based intrusion discrimination in optical fiber perimeter security system

机译：光纤周边安全系统中基于完全建模的入侵识别

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摘要

In order to develop an efficient, accurate and richly functional intrusion discrimination scheme in the optical fiber perimeter security, this paper proposed a fully modelling based scheme for the DMZI vibration system. In this scheme, data modelling is applied in both the feature extraction stage and the pattern classification stage. By means of incorporating the coefficients of AR modelling into the feature vector, the intrusion characteristics are described in a brief, overall and essential way, which helps to recognize more intrusion types than the existing schemes. Moreover, owing to that the sigmoid modelling is applied in the classifier design, the proposed scheme is endowed with a particular function of estimating occurrence probabilities for all intrusion types. Besides, the adoption of AdaBoostSVM technique further enhances the classification rate. Experimental results showed that, with the above techniques incorporated, our proposed discrimination scheme can identify 6 intrusion types with the average classification rate 87.14% using only 4-length feature patterns, which presents vast potentials for DMZI applications.
机译：为了在光纤周界安全方面开发一种高效，准确，功能丰富的入侵识别方案，本文提出了一种基于完全建模的DMZI振动系统方案。在该方案中，数据建模同时应用于特征提取阶段和模式分类阶段。通过将AR建模的系数合并到特征向量中，以简短，整体和必要的方式描述了入侵特征，与现有方案相比，它有助于识别更多入侵类型。此外，由于在分类器设计中采用了S形建模，因此所提出的方案具有特定功能，可以估计所有入侵类型的发生概率。此外，采用AdaBoostSVM技术进一步提高了分类率。实验结果表明，结合上述技术，我们提出的判别方案仅使用4个长度的特征模式就可以识别6种入侵类型，平均分类率为87.14％，这为DMZI应用提供了巨大的潜力。

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