An explainable unsupervised anomaly detection framework for Industrial Internet of Things

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Computers & Security Pub Date : 2024-09-25 DOI:10.1016/j.cose.2024.104130
Yilixiati Abudurexiti , Guangjie Han , Fan Zhang , Li Liu
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引用次数: 0

Abstract

Industrial Internet of Things (IIoT) systems require effective anomaly detection techniques to ensure optimal operational efficiency. However, constructing a suitable anomaly detection framework for IIoT poses challenges due to the scarcity of labeled data. Additionally, most existing anomaly detection frameworks lack interpretability. To tackle these issues, an innovative unsupervised framework based on time series data analysis is proposed. This framework initially detects anomalous patterns in IIoT sensor data by extracting local features. An improved Time Convolutional Network (TCN) and Kolmogorov–Arnold Network (KAN) based Variational Auto-Encoder (VAE) is then constructed to capture long-term dependencies. The framework is trained in an unsupervised manner and interpreted using Explainable Artificial Intelligence (XAI) techniques. This approach offers insightful explanations regarding the importance of features, thereby facilitating informed decision-making and enhancements. Experimental results demonstrate that the framework is capable of extracting informative features and capturing long-term dependencies. This enables efficient anomaly detection in complex, dynamic industrial systems, surpassing other unsupervised methods.
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面向工业物联网的可解释无监督异常检测框架
工业物联网(IIoT)系统需要有效的异常检测技术,以确保最佳的运行效率。然而,由于标注数据稀缺,为 IIoT 构建合适的异常检测框架面临着挑战。此外,大多数现有的异常检测框架缺乏可解释性。为了解决这些问题,我们提出了一种基于时间序列数据分析的创新型无监督框架。该框架最初通过提取局部特征来检测物联网传感器数据中的异常模式。然后,构建基于时间卷积网络(TCN)和科尔莫哥罗德网络(KAN)的改进型变异自动编码器(VAE),以捕捉长期依赖关系。该框架以无监督方式进行训练,并使用可解释人工智能(XAI)技术进行解释。这种方法能就特征的重要性提供有见地的解释,从而促进知情决策和改进。实验结果表明,该框架能够提取信息特征并捕捉长期依赖关系。这使得在复杂、动态的工业系统中进行高效的异常检测成为可能,超越了其他无监督方法。
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
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