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AI and Data Security for the Industrial Internet

Submission Deadline: 31 December 2024 View: 1103 Submit to Special Issue

Guest Editors

Prof. Song Deng, Nanjing University of Posts and Telecommunications, China
Prof. Di Wu, Southwest University, China
Dr. Yi He, Old Dominion University, USA

Summary

Industrial Internet is a new industrial ecology, key infrastructure and new application mode for the deep integration of new-generation information and communication network technology and industrial manufacturing. It realizes the comprehensive connection of all elements of production, the whole industrial chain, and the whole value chain through the safe and reliable intelligent connection of people, machines, and things, promotes the fundamental change of the manufacturing mode of production and enterprise form, forms a brand-new industrial production, manufacturing, and service system, and significantly improves the level of digital, networked, and intelligent development of manufacturing industry.


Traditional industrial production often relies on manual operation, which is inefficient and easily affected by human factors. However, through the application of artificial intelligence technologies such as machine learning and deep learning, industrial equipment can learn and analyze a large amount of data to make decisions and optimize autonomously. This intelligent upgrade makes industrial production more efficient, accurate and reliable, speeding up the production process and reducing production costs. However, data security and privacy protection are important directions that the application of AI in the industrial Internet must focus on. Data acquisition, transmission, storage and analysis in the industrial Internet all involve data security and privacy protection. AI-based industrial Internet applications must ensure data security and compliance in all aspects.


Therefore, this special issue focuses on AI and data security for the industrial internet. The following subtopics are the particular interests of this special issue, including but not limited to:

• AI-based fault diagnosis in the Industrial Internet

• AI-based personalized recommendation in Industrial Internet

• AI-based user behavior analysis in Industrial Internet

• AI-based target detection in Industrial Internet

• Data security with full life cycle in Industrial Internet

• Data privacy protection in Industrial Internet

• Abnormal data detection in Industrial Internet

• Abnormal data recovery in Industrial Internet

• Data encryption in Industrial Internet

• Reliable data storage in Industrial Internet

• Data security audit in Industrial Internet

Keywords

Industrial Internet, AI, Data Mining, Privacy protection, Abnormal Data Detection, Abnormal Data Recovery, Deep Learning, Data Security

Published Papers

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  • Open Access

    ARTICLE

    Advancing Autoencoder Architectures for Enhanced Anomaly Detection in Multivariate Industrial Time Series

    Byeongcheon Lee, Sangmin Kim, Muazzam Maqsood, Jihoon Moon, Seungmin Rho
    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1275-1300, 2024, DOI:10.32604/cmc.2024.054826
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract In the context of rapid digitization in industrial environments, how effective are advanced unsupervised learning models, particularly hybrid autoencoder models, at detecting anomalies in industrial control system (ICS) datasets? This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things (IoT) devices, which can significantly improve the reliability and safety of these systems. In this paper, we propose a hybrid autoencoder model, called ConvBiLSTM-AE, which combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to More >

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    464

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    223

  • Open Access

    REVIEW

    Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence

    Shachar Bar, P. W. C. Prasad, Md Shohel Sayeed
    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1-23, 2024, DOI:10.32604/cmc.2024.053861
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract Escalating cyber security threats and the increased use of Internet of Things (IoT) devices require utilisation of the latest technologies available to supply adequate protection. The aim of Intrusion Detection Systems (IDS) is to prevent malicious attacks that corrupt operations and interrupt data flow, which might have significant impact on critical industries and infrastructure. This research examines existing IDS, based on Artificial Intelligence (AI) for IoT devices, methods, and techniques. The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy, precision, recall and F1-score; this research also… More >

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    812

  • Open Access

    ARTICLE

    Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet

    Qiuyan Wang, Haibing Dong, Yongfei Huang, Zenglei Liu, Yundong Gou
    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1967-1983, 2024, DOI:10.32604/cmc.2024.052775
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract Sharing data while protecting privacy in the industrial Internet is a significant challenge. Traditional machine learning methods require a combination of all data for training; however, this approach can be limited by data availability and privacy concerns. Federated learning (FL) has gained considerable attention because it allows for decentralized training on multiple local datasets. However, the training data collected by data providers are often non-independent and identically distributed (non-IID), resulting in poor FL performance. This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain… More >

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    742

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    280

  • Open Access

    ARTICLE

    A Data Intrusion Tolerance Model Based on an Improved Evolutionary Game Theory for the Energy Internet

    Song Deng, Yiming Yuan
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3679-3697, 2024, DOI:10.32604/cmc.2024.052008
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract Malicious attacks against data are unavoidable in the interconnected, open and shared Energy Internet (EI), Intrusion tolerant techniques are critical to the data security of EI. Existing intrusion tolerant techniques suffered from problems such as low adaptability, policy lag, and difficulty in determining the degree of tolerance. To address these issues, we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas: 1) it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights; and 2) it combines a tournament competition More >

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    727

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    232

  • Open Access

    ARTICLE

    Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

    Tingting Su, Jia Wang, Wei Hu, Gaoqiang Dong, Jeon Gwanggil
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4433-4448, 2024, DOI:10.32604/cmc.2024.051535
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more… More >

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    609

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    227

  • Open Access

    ARTICLE

    A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain

    Chunhui Li, Hua Jiang
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4491-4512, 2024, DOI:10.32604/cmc.2024.048431
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract In the context of enterprise systems, intrusion detection (ID) emerges as a critical element driving the digital transformation of enterprises. With systems spanning various sectors of enterprises geographically dispersed, the necessity for seamless information exchange has surged significantly. The existing cross-domain solutions are challenged by such issues as insufficient security, high communication overhead, and a lack of effective update mechanisms, rendering them less feasible for prolonged application on resource-limited devices. This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload. Within this framework, individual nodes solely engage in… More >

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    490

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    232

  • Open Access

    ARTICLE

    Cluster Detection Method of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

    Ruchun Jia, Jianwei Zhang, Yi Lin, Yunxiang Han, Feike Yang
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2523-2546, 2024, DOI:10.32604/cmc.2024.047543
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract In order to enhance the accuracy of Air Traffic Control (ATC) cybersecurity attack detection, in this paper, a new clustering detection method is designed for air traffic control network security attacks. The feature set for ATC cybersecurity attacks is constructed by setting the feature states, adding recursive features, and determining the feature criticality. The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data. An autoencoder is introduced into the AI (artificial intelligence) algorithm to encode and… More >

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    468

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    234

  • Open Access

    ARTICLE

    Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention

    Yifan Gao, Jieming Zhang, Zhanchen Chen, Xianchao Chen
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 493-507, 2024, DOI:10.32604/cmc.2024.048615
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract In this paper, we propose a novel anomaly detection method for data centers based on a combination of graph structure and abnormal attention mechanism. The method leverages the sensor monitoring data from target power substations to construct multidimensional time series. These time series are subsequently transformed into graph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matrices and additional weights associated with the graph structure, an aggregation matrix is derived. The aggregation matrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features. Moreover, both the More >

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    706

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    432

  • Open Access

    ARTICLE

    Enhancing PDF Malware Detection through Logistic Model Trees

    Muhammad Binsawad
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3645-3663, 2024, DOI:10.32604/cmc.2024.048183
    （This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity, and because of its complexity and evasiveness, it is challenging to identify using traditional signature-based detection approaches. The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses, highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies. The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree. Using a dataset from the Canadian Institute for Cybersecurity, a comparative analysis is carried… More >

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    743

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    309

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