Anomaly Detection Collaborating Adaptive CEEMDAN Feature Exploitation with Intelligent Optimizing Classification for IIoT Sparse Data

Jianming Zhao,Peng Zeng,Qimei Jiang,Xinlu Xu,Jinfang Li,Ming Wan,Yan Huo

doi:10.1155/2021/4329219

Abstract

IIoT (Industrial Internet of Things) has gained considerable attention and has been increasingly applied due to its ubiquitous sensing and communication. However, the sparse characteristic of sensing data in distributed IIoT networks may bring out tremendous challenges to implement the security protection measures. Based on the design of centralized data gathering and forwarding, this paper proposes a novel anomaly detection approach for IIoT sparse data, which can successfully collaborate the adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) feature exploitation with one intelligent optimizing classification. Furthermore, in the adaptive CEEMDAN feature exploitation, the CEEMDAN energy entropy based on adaptive IMF (Intrinsic Mode Function) selection is designed to extract the sensing features from IIoT sparse data; in the intelligent optimizing classification, one effective OCSVM (One-Class Support Vector Machine) classifier optimized by the IABC (Improved Artificial Bee Colony) swarm intelligence algorithm is introduced to detect various abnormal sensing features. The experimental results show that, not only does the CEEMDAN energy entropy based on adaptive IMF selection accurately describe the change of industrial production by analyzing the probability distribution and energy distribution of sparse sensing data, but also the proposed IABC-OCSVM classifier has higher detection efficiency compared with the OCSVM classifiers optimized by other swarm intelligence algorithms.

Highlights

IIoT (Industrial Internet of Things), which can effectively implement real-time simulation and remote control during the whole production or manufacturing cycle, has been regarded as an important driving force in the industrial intelligent revolution [1]
The CEEMDAN energy entropy based on adaptive IMF (Intrinsic Mode Function) selection is designed to extract the sensing features from IIoT sparse data, and one effective OCSVM (One-Class Support Vector Machine) classifier optimized by the IABC (Improved Artificial Bee Colony) swarm intelligence algorithm is introduced to detect various abnormal sensing features
We use some real-world data captured from one local oilfield IIoT system in the northeastern part of China to evaluate our approach, and the experimental results show that, for one thing, compared with the traditional CEEMDAN singular spectrum entropy and EEMD singular value decomposition, the CEEMDAN energy entropy based on adaptive IMF selection can accurately describe the change of sparse sensing data and is more sensitive to the size of abnormal data; for another, compared with the OCSVM classifiers optimized by other swarm intelligence algorithms, the proposed IABC-OCSVM classifier has higher detection efficiency

Summary

Introduction

IIoT (Industrial Internet of Things), which can effectively implement real-time simulation and remote control during the whole production or manufacturing cycle, has been regarded as an important driving force in the industrial intelligent revolution [1]. Based on the relatively short-range communication characteristic, most IIoT systems always utilize the data collector to gather and forward the sensing data from distributed IIoT sensors, and this design can contribute to developing an experienced machine-learning anomaly detection model, which can thoroughly explore the statefulness and correlation characteristics of sparse sensing data. From this point of view, this paper proposes a novel anomaly detection approach for IIoT sparse data, and this approach successfully collaborates the adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) feature exploitation with one intelligent optimizing classification. Describe the change of sparse sensing data and is more sensitive to the size of abnormal data; for another, compared with the OCSVM classifiers optimized by other swarm intelligence algorithms, the proposed IABC-OCSVM classifier has higher detection efficiency

Adaptive CEEMDAN Feature Exploitation

IABC-OCSVM Anomaly Detection Classifier

Experimental Evaluation and Discussion

Findings

Conclusions