论文标题
学会检测物联网网络中的无线链接
Learning to Detect Anomalous Wireless Links in IoT Networks
论文作者
论文摘要
经过数十年的研究，物联网（IoT）终于渗透到现实生活中，并有助于提高基础设施和流程的效率以及我们的健康。由于部署了大量的物联网设备，因此它们自然会产生巨大的运营成本以确保预期的操作。为了有效地处理大型物联网网络中的这种预期操作，自动检测故障，即异常检测，成为一项至关重要但充满挑战的任务。在本文中，是由现实世界实验物联网部署的动机，我们介绍了四种类型的无线网络异常，这些异常在链接层上识别。我们研究阈值和机器学习（ML）基于分类器的性能，以自动检测这些异常。我们检查了未编码和编码（自动编码器）特征表示的三个监督和三种无监督的ML技术的相对性能。我们的结果表明了这一点； i）选定的有监督方法能够检测到F1得分高于0.98的异常情况，而无监督的方法也能够检测到F1得分平均为0.90和ii的上述异常，OC-SVM在0.99中均超过0.99的0.99 forsdddend，OC-SVM超过了所有其他无人保证的ML方法。慢速。
After decades of research, the Internet of Things (IoT) is finally permeating real-life and helps improve the efficiency of infrastructures and processes as well as our health. As a massive number of IoT devices are deployed, they naturally incur great operational costs to ensure intended operations. To effectively handle such intended operations in massive IoT networks, automatic detection of malfunctioning, namely anomaly detection, becomes a critical but challenging task. In this paper, motivated by a real-world experimental IoT deployment, we introduce four types of wireless network anomalies that are identified at the link layer. We study the performance of threshold- and machine learning (ML)-based classifiers to automatically detect these anomalies. We examine the relative performance of three supervised and three unsupervised ML techniques on both non-encoded and encoded (autoencoder) feature representations. Our results demonstrate that; i) selected supervised approaches are able to detect anomalies with F1 scores of above 0.98, while unsupervised ones are also capable of detecting the said anomalies with F1 scores of, on average, 0.90, and ii) OC-SVM outperforms all the other unsupervised ML approaches reaching at F1 scores of 0.99 for SuddenD, 0.95 for SuddenR, 0.93 for InstaD and 0.95 for SlowD.
