An Energy-efficient And Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT
An Energy-efficient And Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT
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DOI：
10.1145/3543855
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发表时间：
2022-11-01
影响因子：
4.1
通讯作者：
Yin, Hao
中科院分区：
文献类型：
--
作者：
Huang, Zijie;Wu, Yulei;Yin, Hao
工业物联网（IIoT）采用了许多异常检测技术来提高自诊断效率和基础设施安全性。然而，它们通常与计算饥饿和"黑盒"问题有关。"因此，确保检测不仅准确，而且节能和值得信赖变得非常重要。在这篇文章中，我们提出了一个适用于IIoT的节能且值得信赖的无监督异常检测框架（EATU）。该框架由两个级别的特征提取组成：（1）基于Autoencoder的特征提取和（2）基于Efficient DeepExplainer的可解释特征选择。我们提出了一种基于扰动聚焦采样的高效DeepExplainer模型，它在最先进的可解释模型中表现出最高的计算效率。利用Efficient DeepExplainer选择的重要特征，给出了异常检测决策的依据，增强了检测的可信度，提高了异常检测的准确性。使用三个具有高维特征的真实IIoT数据集来验证所提出的框架的有效性。大量的实验结果表明，与最先进的相比，我们的框架具有提高的准确性，可信度（在正确性和稳定性的解释），和能源效率（在挂钟时间和资源使用方面）的属性。
Many anomaly detection techniques have been adopted by Industrial Internet of Things (IIoT) for improving self-diagnosing efficiency and infrastructures security. However, they are usually associated with the issues of computational-hungry and "black box." Thus, it becomes important to ensure that the detection is not only accurate but also energy-efficient and trustworthy. In this article, we propose an Energy-efficient And Trustworthy Unsupervised anomaly detection framework (EATU) for IIoT. The framework consists of two levels of feature extraction: (1) Autoencoder-based feature extraction and (2) Efficient DeepExplainer-based explainable feature selection. We propose an Efficient DeepExplainer model based on perturbation-focused sampling, which demonstrates the most computational efficiency among state-of-the-art explainable models. With the important features selected by Efficient DeepExplainer, the rationale of why an anomaly detection decision was made is given, enhancing the trustworthiness of the detection as well as improving the accuracy of anomaly detection. Three real-world IIoT datasets with high-dimensional features are used to validate the effectiveness of the proposed framework. Extensive experimental results demonstrate that in comparison with the state-of-the-art, our framework has the attributes of improved accuracy, trustworthiness (in terms of correctness and stability of the explanation), and energy-efficiency (in terms of wall-clock-time and resource usage).