The intelligent industrial environment developed with the support of the new generation network physics system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed. It builds an evaluation framework based on knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real-time. Preventive measures can be taken timely. And the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. based on this, we propose a cps trusted robust intelligent control strategy and a trusted intelligent prediction model. Through simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in distributed cooperative control mode can be guided and select appropriate defense resource input according to the cps attack and defense environment.
- CPS and AI,
- Internet of Things,
- Trustworthiness model,
- Against and Attack,
- Industrial Environments
- Mathematical model,
- Analytical models,
- Control systems,
- Time measurement,
- Differential equations
CPS includes the Internet of Things, information physical integration energy system or energy Internet, smart grid, intelligent transportation system, intelligent manufacturing system Intelligent logistics systems, etc., have become the core technology to support and lead a new round of industrial transformation[4-5]. In artificial intelligence and machine learning, data and application scenarios are very important. If we can abstract the application scenario into a model, use Data, combined with the correct algorithm, then information trusted applications will be great for network information credibility has become one of the key issues of Internet development and application whether artificial intelligence can be successfully applied to the trusted field, involving Several measurable factors: adaptability, interoperability, and enforceability of algorithmic training [6-10]. Traditional physical systems (such as industrial control systems, drive systems, medical devices, etc.) are relatively difficult to access, penetrate, and enforce attacks by attackers because of their relatively isolated operating environment and the use of dedicated channels for communication. Due to the need of informatization and intelligence, traditional physical systems have gradually evolved into cps, and the operating environment of the system has been opened and interconnected through closure and isolation[11-13]. While improving operational efficiency, it also provides new attack channels for attackers, making CPS more vulnerable to internal or external attacks. For example, Stuxnet attacks invade nuclear facility control systems through U disk ferries, while WindShark directly accesses through physics. Manned Wind Farm Control System Once an attacker breaks through the CPS network boundary and enters the internal network, the attack success rate may be higher than the Internet attack, and the threat may be greater [14-17]. Most physical systems design fault diagnosis and safety emergency measures from an engineering safety perspective. For example, the Stuxnet attack tampers with the reported system measurement data, making the control center unable to detect system anomalies, black energy destroys the system communication module and uses denial of service Attacks interfere with grid calls [18-20]. The service system makes the system’s default emergency response and defense strategies not implemented effectively. The common features of these attacks are: formulating corresponding attack strategies for specific business processes and trusted plans of physical systems, using network attack technology to implement coordinated attacks on multiple targets, and bypassing physical trusted systems to disrupt the normal operation of the system. Even destroying physical equipment[21-24]. In the future, based on artificial intelligence-based autonomous learning and powerful database analysis capabilities, people can anticipate dangers in advance and truly kill threats in the cradle, thereby greatly improving the agility of network physical credibility. The credible control of CPS is shifting from physical credibility to physical and social factors integrated disaster prevention[25-28]. However, due to the characteristics of CPS, such as large-scale data processing, continuous on-line operation of the system, operators can only conduct closed feedback, and so on, it is urgent to realize the real-time reliability evaluation of CPS. To promote the in-depth integration of informatization and industrialization is to profoundly grasp the characteristics of the era of global informationization and the process of accelerating the convergence of industrialization.
In this paper, the reliability of CPS is studied, and an automatic online evaluation method of CPS reliability based on machine learning is proposed. In one step, the work mainly focuses on the CPS model, algorithm, and implementation tools. With the continuous development of CPS in recent years, the development of high real-time, automatic, and intelligent CPS has gradually become the common demand of various industries. With the application of information network technology such as sensor, embedded processing, digital communication, artificial intelligence and so on, the various vulnerabilities and vulnerabilities in the information network have seriously affected the safe operation of CPS, and have caused great losses while improving the performance and efficiency of existing systems. With the maturity of artificial intelligence technology, the application of artificial intelligence in the field of network physical space credibility (AI CPS) cannot only improve the response speed and response speed of various threats in cyberspace but also improve the predictability and accuracy of risk prevention in an all-round way. Therefore, artificial intelligence technology has been fully applied in the field of CPS and has played great potential in dealing with all kinds of human integrity problems in the intelligent era.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
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FULL Paper PDF file:Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence
Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence,
in IEEE Transactions on Industrial Informatics
PDF reference and original file: Click here
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.