Research On Several Key Issues In Industrial Big Data Security Management

Industry plays a pivotal role in the supply-side structural reform and digital transformation,bearing the responsibility of achieving the Two Centenary Goals.Currently,digital transformation in the industrial sector in China is still in its early stages.Big data,cloud computing,artificial intelligence,the Internet of Things,and 5G are accelerating into the manufacturing industry.More and more industrial companies are benefiting from the quality improvement and efficiency enhancement brought about by digitization,networking,intelligence,and platformization.Data,which pervades throughout the process,has become a strategic resource of the nation,the newest factor of production,and data security has been pushed to the forefront of great power competition.Meanwhile,how to achieve security management of industrial big data has become the focus of attention in the government,industry,and academic fields during the rapid development of digital transformation in industry and the digital economy.Security management of industrial big data involves multiple aspects,including data collection,transmission,sharing,application,industry,cross-border,and multiple stakeholders from government,industry,academia,and research.In recent years,lots of legal and policy developments related to industrial big data security management have emerged,and academic research has also seen a rapid increase.This paper has systematically reviewed the relevant works and conducted in-depth research on several key aspects and hot issues in industrial big data security management,including the study of main body strategies to promote security management of industrial big data and the research of key management and technical issues in the "generation-processingsharing-cross-border" process of industrial big data.This paper provides management and technical ideas for the security management of industrial big data,and the main contents and innovations of this paper are as follows:Firstly,this paper addresses the issue of security management of industrial big data by introducing the three-party evolutionary game model into the research field of industrial big data security management strategies for the first time.By combining the two aspects of social and economic benefits,the paper defines the government,security companies,and industrial companies as the three stakeholders in industrial big data security management,analyzes the various game relationships and corresponding gains and losses faced by the three parties,and obtains the three-party gain and loss matrix to construct the three-party evolutionary game model for security management of industrial big data.Based on the above basic model and parameters,the paper conducts dynamic and stable path analysis of the government strategy,industrial company strategy,and security company strategy in the game model,calculates the corresponding Pareto optimal equilibrium strategies under different strategies,and finally conducts simulation and emulation of the examples constructed in the three-party evolutionary model,and provides corresponding countermeasures based on the analysis results.The result is also the overall research leader for the subsequent content of this article.Secondly,with respect to the issue of security in the stage of industrial big data generation,various industrial software such as industrial information systems,application software,middleware,and embedded systems are used for data collection,transmission,and processing,and face a wide range of attack surfaces.This study proposes a large-scale industrial software accessibility security analysis algorithm that can be well-suited for high-complexity large-scale software network security analysis in the current industrial field.By comparing the processing indicators of large-scale graph networks with other solutions,it was found that in the aspect of security analysis of large-scale software nodes,this algorithm greatly improves the calculation speed of accessibility analysis,solving the problem of low efficiency in obtaining accessibility matrices using traditional methods,which cannot be applied to large-scale software networks.This provides support for enhancing the security level of industrial big data.Thirdly,with respect to the issue of classification and grading management in the processing stage of industrial big data,this paper is based on the three-tuple data classification and grading idea of availability,integrity,and confidentiality security.The paper adopts the data protection impact assessment method and optimizes the design of the layered classification support vector machine algorithm to propose an AIC-ASVM classification and grading method that is suitable for the requirements of industrial big data management.The proposed method was applied to the data classification and grading practice of a steel company,and the verification results showed that the AIC-ASVM model achieved an accuracy rate of 96.7%.Compared with relevant algorithms,this approach simplifies the implementation while ensuring accuracy,and improves the efficiency of industrial big data classification and grading,providing a solid foundation for companies to further develop comprehensive data management.Fourthly,regarding the issue of industrial big data exchange and sharing,this paper proposes data security risk information as an example,and studies the blockchain-based industrial big data sharing architecture and its’ key issues.The paper designs a blockchain-based industrial big data sharing basic model and defines industrial big data reporting and transaction scenarios based on this model.The paper also optimizes and improves the key protocols and algorithms for the designed model.To address the problem of balancing user privacy protection and data usability during the data sharing and reporting process,the paper proposes a lightweight data submission linkable ring signature algorithm.This algorithm effectively reduces the signature size and generates and verifies user signatures safely without exchanging public key certificates and key directories between the communicating parties.It is a solution to the problem of balancing user privacy protection and data usability during the data sharing and reporting process.In order to address the issue of legitimate transaction loss during data cross-chain sharing and exchange,this paper proposes a data cross-chain atomic exchange protocol to ensure the reliability of data exchange.This protocol does not require script execution and can solve the problem of malicious users creating a large number of timeout transactions that cause network congestion.To solve the problem of fairness loss among data trading participants due to the asynchronous nature of the network and the lack of physical connections between blockchain nodes for industrial big data reporting,the paper designs a data sharing fair trade protocol.This protocol utilizes the smart contract mechanism of the blockchain to automatically verify and execute transaction conditions,ensuring fair data transaction between data reporters and users,as well as protecting the interests of intermediate users along the transaction path.Last but not least,with regards to cross-border data management,this paper comprehensively reviews and compares the industrial big data cross-border management policies and mechanisms of major countries and regions around the world,analyzes the security risks faced in cross-border data cooperation,and creatively combines the international backgrounds,motivations with value orientations,regulatory paths,and industrial big data characteristics of different countries and regions in studying the international cooperation model for industrial big data management,thereby addressing the insufficient consideration of industrial sector characteristics in existing research on cross-border data management.Based on the above analysis results and China’s conditions,this paper proposes suggestions for industrial big data cross-border management and international cooperation that follow the principle of laying equal stress on security and development.Based on the research on managements and technologies covering the production,processing,sharing,cross-border processing,and management strategy of industrial big data,effective management and technical support are provided for industrial big data security.