Intrusion Detection Based on Device-Free Localization in the Era of IoT
Intrusion Detection Based on Device-Free Localization in the Era of IoT
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DOI：
10.3390/sym11050630
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发表时间：
2019-05
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通讯作者：
Lingjun Zhao-;Chunhua Su;Huakun Huang;Zhaoyang Han;Shuxue Ding;Xiang Li
中科院分区：
文献类型：
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作者：
Lingjun Zhao-;Chunhua Su;Huakun Huang;Zhaoyang Han;Shuxue Ding;Xiang Li
无设备定位技术（DFL）在不需要设备的情况下对目标进行定位，对于物联网时代的入侵检测或监控具有重要意义。针对DFL方法精度低、鲁棒性差的问题，首先将RSS信号作为RSS图像矩阵进行背景剔除，提取具有显著特征的变化分量。然后，我们利用这些功能丰富的图像制定DFL作为一个图像分类问题。此外，设计了深度卷积神经网络（CNN）来自动提取特征进行分类。所提出的基于背景消除的CNN（BE-CNN）方案的定位性能与室外DFL的真实世界数据集进行了验证。此外，我们还验证了该建议的鲁棒性能进行数值实验与不同程度的噪音。实验结果表明，该方法在提高DFL定位精度和鲁棒性方面具有明显的优势。特别是，BE-CNN可以保持100%的最高定位精度，即使在SNR超过-5 dB的噪声条件下也是如此。在定位精度方面，基于BE的方法可以优于所有相应的基于原始数据的方法。此外，所提出的方法可以优于比较方法，具有自动编码器的深度神经网络，K-最近邻（KNN），支持向量机（SVM）等，在定位精度和鲁棒性方面。
Device-free localization (DFL) locates targets without being equipped with the attached devices, which is of great significance for intrusion detection or monitoring in the era of the Internet-of-Things (IoT). Aiming at solving the problems of low accuracy and low robustness in DFL approaches, in this paper, we first treat the RSS signal as an RSS-image matrix and conduct a process of eliminating the background to dig out the variation component with distinguished features. Then, we make use of these feature-rich images by formulating DFL as an image classification problem. Furthermore, a deep convolutional neural network (CNN) is designed to extract features automatically for classification. The localization performance of the proposed background elimination-based CNN (BE-CNN) scheme is validated with a real-world dataset of outdoor DFL. In addition, we also validate the robust performance of the proposal by conducting numerical experiments with different levels of noise. Experimental results demonstrate that the proposed scheme has an obvious advantage in terms of improving localization accuracy and robustness for DFL. Particularly, the BE-CNN can maintain the highest localization accuracy of 100%, even in noisy conditions when the SNR is over −5 dB. The BE-based methods can outperform all the corresponding raw data-based methods in terms of the localization accuracy. In addition, the proposed method can outperform the comparison methods, deep neural network with autoencoder, K-nearest-neighbor (KNN), support vector machines (SVM), etc., in terms of the localization accuracy and robustness.