Special Issues
Table of Content

AI and Data Security for the Industrial Internet

Submission Deadline: 31 August 2024 View: 171 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


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


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

Published Papers

  • Open Access


    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 >

  • Open Access


    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 >

  • Open Access


    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 >

  • Open Access


    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 >

  • Open Access


    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 >

  • Open Access


    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 >

Share Link