Various types of sensor data can be collected by the Internet of Things (IoT). Each sensor node has spatial attributes and may also be associated with a large number of measurement data that evolve over time; therefore, these high-dimensional sensor data are inherently large scale. Detecting outliers in large-scale IoT sensor data is a challenging task. Most existing anomaly detection methods are based on a vector representation. However, large-scale IoT sensor data have characteristics that make tensor methods more efficient for extracting information. The vector-based methods can destroy original structural information and correlation within large-scale sensor data, resulting in the problem of the “curse of dimensionality,” and some outliers hence cannot be detected. In this paper, we propose a one-class support Tucker machine (OCSTuM) and an OCSTuM based on tensor Tucker factorization and a genetic algorithm called GA-OCSTuM. These methods extend one-class support vector machines to tensor space. OCSTuM and GA-OCSTuM are unsupervised anomaly detection approaches for big sensor data. They retain the structural information of data while improving the accuracy and efficiency of anomaly detection. The experimental evaluations on real data sets demonstrate that our proposed method improves the accuracy and efficiency of anomaly detection while retaining the intrinsic structure of big sensor data.

中文翻译：

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基于一类支持Tucker机和遗传算法的物联网大传感器数据智能异常检测方法

物联网 (IoT) 可以收集各种类型的传感器数据。每个传感器节点都有空间属性，也可能与大量随时间演化的测量数据相关联；因此，这些高维传感器数据本质上是大规模的。检测大规模物联网传感器数据中的异常值是一项具有挑战性的任务。大多数现有的异常检测方法都是基于向量表示的。然而，大规模物联网传感器数据具有使张量方法更有效地提取信息的特性。基于向量的方法会破坏大规模传感器数据中的原始结构信息和相关性，从而导致“维数灾难”的问题，从而无法检测到一些异常值。在本文中，我们提出了一类支持塔克机 (OCSTuM) 和基于张量塔克分解的 OCSTuM 和称为 GA-OCSTuM 的遗传算法。这些方法将一类支持向量机扩展到张量空间。OCSTuM 和 GA-OCSTuM 是用于大传感器数据的无监督异常检测方法。它们保留了数据的结构信息，同时提高了异常检测的准确性和效率。对真实数据集的实验评估表明，我们提出的方法提高了异常检测的准确性和效率，同时保留了大传感器数据的内在结构。OCSTuM 和 GA-OCSTuM 是用于大传感器数据的无监督异常检测方法。它们保留了数据的结构信息，同时提高了异常检测的准确性和效率。对真实数据集的实验评估表明，我们提出的方法提高了异常检测的准确性和效率，同时保留了大传感器数据的内在结构。OCSTuM 和 GA-OCSTuM 是用于大传感器数据的无监督异常检测方法。它们保留了数据的结构信息，同时提高了异常检测的准确性和效率。对真实数据集的实验评估表明，我们提出的方法提高了异常检测的准确性和效率，同时保留了大传感器数据的内在结构。