Abstract For establishing an accurate and reliable distributed optical fiber perimeter security system, this paper proposes a novel abnormity detection solution to security using Recurrent Plot (RP) and deep learning technology. Take advantage of the temporal correlation of intrusion signals, we encode the sensing signals into two-dimensional images through the RP algorithm. The RP algorithm can extract the motion characteristics of the signal from the complex time series, and it is robust to instrument noise. These encoded image signatures can reveal the deeper temporal correlation of the intrusion signals’ motion. After that, Inception network can adaptively extract the features of these images to complete the accurate identification of a series of noisy intrusion signals. We conducted experiments on three most frequent natural events and three representative man-made intrusion events, including heavy rain, light rain, wind blowing, treading, slapping, and impacting. The results show that the detection accuracy has reached 99.7%. This method can achieve 0.35 s real-time detection in the online detection of abnormal events while ensuring accuracy, providing a new intrusion pattern identification idea for perimeter security.

中文翻译：

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基于RP和Inception网络的分布式光纤周界系统异常事件检测

摘要 为了建立准确可靠的分布式光纤周界安防系统，本文提出了一种利用循环图（RP）和深度学习技术进行安全异常检测的新解决方案。利用入侵信号的时间相关性，我们通过RP算法将感知信号编码成二维图像。RP算法可以从复杂的时间序列中提取信号的运动特征，对仪器噪声具有鲁棒性。这些编码的图像签名可以揭示入侵信号运动的更深层次的时间相关性。之后，Inception 网络可以自适应地提取这些图像的特征，完成对一系列噪声入侵信号的准确识别。我们对三个最频繁的自然事件和三个具有代表性的人为入侵事件进行了实验，包括大雨、小雨、刮风、踩踏、拍打和撞击。结果表明，检测准确率达到了99.7%。该方法在保证准确性的同时，在异常事件的在线检测中可以实现0.35s的实时检测，为周界安全提供了一种新的入侵模式识别思路。