Biblio

Artificial technology developed in recent years. It is an intelligent system that can perform tasks without human intervention. AI can be used for various purposes, such as speech recognition, face recognition, etc. AI can be used for good or bad purposes, depending on how it is implemented. The discuss the application of AI in data security technology and its advantages over traditional security methods. We will focus on the good use of AI by analyzing the impact of AI on the development of big data security technology. AI can be used to enhance security technology by using machine learning algorithms, which can analyze large amounts of data and identify patterns that cannot be detected automatically by humans. The computer big data security technology platform based on artificial intelligence in this paper is the process of creating a system that can identify and prevent malicious programs. The system must be able to detect all types of threats, including viruses, worms, Trojans and spyware. It should also be able to monitor network activity and respond quickly in the event of an attack.

Intelligent Systems for Personal Data Cyber Security is a critical component of the Personal Information Management of Medicaid Enterprises. Intelligent Systems for Personal Data Cyber Security combines components of Cyber Security Systems with Human-Computer Interaction. It also uses the technology and principles applied to the Internet of Things. The use of software-hardware concepts and solutions presented in this report is, in the authors’ opinion, some step in the working-out of the Intelligent Systems for Personal Data Cyber Security in Medicaid Enterprises. These concepts may also be useful for developers of these types of systems.

In the field of airport passenger security, a new type of security inspection equipment called intelligent passenger security equipment is applied widely, which can significantly improve the efficiency of airport security screening and passenger satisfaction. This paper establishes a security check channel model based on intelligent passenger security check equipment, and studies the factors affecting the efficiency of airport security screening, such as the number of baggage unloading points, baggage loading points, secondary inspection points, etc. A simulation model of security check channel is established based on data from existing intelligent passenger security check equipment and data collected from Beijing Daxing Airport. Equipment utilization and queue length data is obtained by running the simulation model. According to the data, the bottleneck is that the manual inspection process takes too long, and the utilization rate of the baggage unloading point is too low. For the bottleneck link, an optimization scheme is proposed. With more manual check points and secondary inspection points and less baggage unloading points, the efficiency of airport security screening significantly increases by running simulation model. Based on the optimized model, the effect of baggage unloading point and baggage loading point on efficiency is further studied. The optimal parameter configuration scheme under the expected efficiency is obtained. This research can assist engineers to find appropriate equipment configuration quickly and instruct the airport to optimize the arrangement of security staff, which can effectively improve the efficiency of airport security screening and reduce the operating costs of airport.

Tourism is one of the main sources of income in Australia. The number of tourists will affect airlines, hotels and other stakeholders. Predicting the arrival of tourists can make full preparations for welcoming tourists. This paper selects Queensland Tourism data as intelligent data. Carry out data visualization around the intelligent data, establish seasonal ARIMA model, find out the characteristics and predict. In order to improve the accuracy of prediction. Based on the tourism data around Queensland, build a 10 layer Back Propagation neural network model. It is proved that the network shows good performance for the data prediction of this paper.

Problems such as the increase in the number of private vehicles with the population, the rise in environmental pollution, the emergence of unmet infrastructure and resource problems, and the decrease in time efficiency in cities have put local governments, cities, and countries in search of solutions. These problems faced by cities and countries are tried to be solved in the concept of smart cities and intelligent transportation by using information and communication technologies in line with the needs. While designing intelligent transportation systems (ITS), beyond traditional methods, big data should be designed in a state-of-the-art and appropriate way with the help of methods such as artificial intelligence, machine learning, and deep learning. In this study, a data-driven decision support system model was established to help the business make strategic decisions with the help of intelligent transportation data and to contribute to the elimination of public transportation problems in the city. Our study model has been established using big data technologies and business intelligence technologies: a decision support system including data sources layer, data ingestion/ collection layer, data storage and processing layer, data analytics layer, application/presentation layer, developer layer, and data management/ data security layer stages. In our study, the decision support system was modeled using ITS data supported by big data technologies, where the traditional structure could not find a solution. This paper aims to create a basis for future studies looking for solutions to the problems of integration, storage, processing, and analysis of big data and to add value to the literature that is missing within the framework of the model. We provide both the lack of literature, eliminate the lack of models before the application process of existing data sets to the business intelligence architecture and a model study before the application to be carried out by the authors.

The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.

Nowadays, smart cities (SCs) use technologies and different types of data collected to improve the lifestyles of their citizens. Indeed, connected smart vehicles are technologies used for an SC’s intelligent traffic monitoring systems (ITMSs). However, most proposed monitoring approaches do not consider realtime monitoring. This paper presents real-time data processing for an intelligent traffic monitoring dashboard using the Hadoop ecosystem dashboard components. Many data are available due to our proposed monitoring approach, such as the total number of vehicles on different routes and data on trucks within a radius (10KM) of a specific point given. Based on our generated data, we can make real-time decisions to improve circulation and optimize traffic flow.

The application of mobile intelligent terminal in the environment is very complex, and its own computing capacity is also very limited, so it is vulnerable to malicious attacks. The security classification of mobile intelligent terminals can effectively ensure the security of their use. Therefore, a security classification method for mobile intelligent terminals based on multi-source data fusion is proposed. The Boolean value is used to count the multi-source data of the mobile intelligent terminal, and the word frequency method is used to calculate the weight of the multi-source data of the mobile intelligent terminal. The D-S evidence theory is used to complete the multi-source data fusion of the mobile intelligent terminal and implement the multi-source data fusion processing of the mobile intelligent terminal. On this basis, the security level permission value of mobile intelligent terminal is calculated to achieve the security level division of mobile intelligent terminal based on multi-source data fusion. The experimental results show that the accuracy of mobile intelligent terminal security classification is higher than 96% and the classification time is less than 3.8 ms after the application of the proposed method. Therefore, the security level of mobile intelligent terminals after the application of this method is high, and the security performance of mobile intelligent terminals is strong, which can effectively improve the accuracy of security classification and shorten the time of security classification.

The introduction of the study primarily emphasises the significance of utilising block chain technologies with the possibility of privacy and security benefits from the 5G Network. One may state that the study’s primary focus is on all the advantages of adopting block chain technology to safeguard everyone’s access to crucial data by utilizing intelligent contracts to enhance the 5G network security model on information security operations.Our literature evaluation for the study focuses primarily on the advantages advantages of utilizing block chain technology advance data security and privacy, as well as their development and growth. The whole study paper has covered both the benefits and drawbacks of employing the block chain technology. The literature study part of this research article has, on the contrary hand, also studied several approaches and tactics for using the blockchain technology facilities. To fully understand the circumstances in this specific case, a poll was undertaken. It was possible for the researchers to get some real-world data in this specific situation by conducting a survey with 51 randomly selected participants.

With the rapid development of urbanization, community security and public security have become an important social issue. As conventional patrol methods can not effectively ensure effective supervision, this paper studies the application of NFC (Near Field Communication) technology in intelligent security system, designs and constructs a set of intelligent security system suitable for public security patrol or security patrol combined with current cloud service technology. The system can not only solve the digital problem of patrol supervision in the current public security, but also greatly improve the efficiency of security and improve the service quality of the industry through the application of intelligent technology.

In order to improve the accuracy and efficiency of transmission line safety early warning, a transmission line safety early warning system based on big data variable analysis is proposed. Firstly, the overall architecture of the system is designed under the B / S architecture. Secondly, in the hardware part of the system, the security data real-time monitoring module, data transmission module and security warning module are designed to meet the functional requirements of the system. Finally, in the system software design part, the big data variable analysis method is used to calculate the hidden danger of transmission line safety, so as to improve the effectiveness of transmission safety early warning. The experimental results show that, compared with the traditional security early warning system, the early warning accuracy and efficiency of the designed system are significantly improved, which can ensure the safe operation of the transmission line.

With powerful ability to organize, retrieve and share information, cloud computing technology has effectively improved the development of intelligent learning ecological Communities. The study finds development create a security atmosphere with all homomorphic encryption technology, virtualization technology to prevent the leakage and loss of information data. The result provided a helpful guideline to build a security environment for intelligent ecological communities.

This paper summarizes the types and contents of enterprise big data information, analyzes the demand and characteristics of enterprise shared data information based on the Internet of things, and analyzes the current situation of enterprise big data fusion at home and abroad. Firstly, using the idea of the Internet of things for reference, the intelligent sensor is used as the key component of data acquisition, and the multi energy data acquisition technology is discussed. Then the data information of entity enterprises is taken as the research object and a low energy consumption transmission method based on data fusion mechanism for industrial ubiquitous Internet of things is proposed. Finally, a network monitoring and data fusion platform for the industrial Internet of things is implemented. The monitoring node networking and platform usability test are also performed. It is proved that the scheme can achieve multi parameter, real-time, high reliable network intelligent management.

The application of intelligent video terminal has spread in all aspects of production and life, such as urban transportation, enterprises, hospitals, banks, and families. In recent years, intelligent video terminals, video recorders and other video monitoring system components are frequently exposed to high risks of security vulnerabilities, which is likely to threaten the privacy of users and data security. Therefore, it is necessary to strengthen the security research and testing of intelligent video terminals, and formulate reinforcement and protection strategies based on the evaluation results, in order to ensure the confidentiality, integrity and availability of data collected and transmitted by intelligent video terminals.

With the rapid construction and popularization of smart grid, the security of data in smart grid has become the basis for the safe and stable operation of smart grid. This paper proposes a data security threat discovery model for smart grid. Based on the prediction data analysis method, combined with migration learning technology, it analyzes different data, uses data matching process to classify the losses, and accurately predicts the analysis results, finds the security risks in the data, and prevents the illegal acquisition of data. The reinforcement learning and training process of this method distinguish the effective authentication and illegal access to data.

Secret-related carrier intelligent management and control system uses the Internet of Things and artificial intelligence to solve the transformation of secret-related carrier management and control from manual operation to automatic detection, precise monitoring, and intelligent decision-making, and use technical means to resolve security risks. However, the coexistence of multiple heterogeneous networks will lead to various network security problems in the secret carrier intelligent management and control. Aiming at the actual requirements of the intelligent management and control of secret-related carriers, this paper proposes a system structure including device domain, network domain, platform domain and user domain, and conducts a detailed system security analysis, and introduces intelligent agent technology, and proposes a distributed system. The hierarchical system structure of the secret-related carrier intelligent management and control security protection model has good robustness and portability.

Intelligent networked vehicles are rapidly developing in intelligence and networking. The communication architecture is becoming more complex, external interfaces are richer, and data types are more complex. Different from the information security of the traditional Internet of Things, the scenarios that need to be met for the security of the Internet of Vehicles are more diverse and the security needs to be more stable. Based on the security technology of traditional Internet of Things, password application is the main protection method to ensure the privacy and non-repudiation of data communication. This article mainly elaborates the application of security protection methods using password-related protection technologies in car-side scenarios and summarizes the security protection recommendations of contemporary connected vehicles in combination with the secure communication architecture of the Internet of Vehicles.

Current power sensitive data security protection adopts classification and grading protection. Company classification and grading are mainly in formulating specifications. Data classification and grading processing is carried out manually, which is heavy and time-consuming, while traditional data identification mainly relies on rules for data identification, the level of automation and intelligence is low, and there are many problems in recognition accuracy. Data classification and classification is the basis of data security protection. Sensitive data identification is the key to data classification and classification, and it is also the first step to achieve accurate data security protection. This paper proposes an intelligent identification and tracking technology of sensitive data for electric power big data, which can improve the ability of data classification and classification, help the realization of data classification and classification, and provide support for the accurate implementation of data security capabilities.

Security patterns are proven solutions to recurring problems in software development. The growing importance of secure software development has introduced diverse research efforts on security patterns that mostly focused on classification schemes, evolution and evaluation of the patterns. Despite a huge mature history of research and popularity among researchers, security patterns have not fully penetrated software development practices. Besides, software security education has not been benefited by these patterns though a commonly stated motivation is the dissemination of expert knowledge and experience. This is because the patterns lack a simple embodiment to help students learn about vulnerable code, and to guide new developers on secure coding. In order to address this problem, we propose to conduct intelligent data mining in the context of software engineering to discover learner-friendly software security patterns. Our proposed model entails knowledge discovery from large scale published real-world vulnerability histories in software applications. We harness association rule mining for frequent pattern discovery to mine easily comprehensible and explainable learner-friendly rules, mainly of the type "flaw implies fix" and "attack type implies flaw", so as to enhance training in secure coding which in turn would augment secure software development. We propose to build a learner-friendly intelligent tutoring system (ITS) based on the newly discovered security patterns and rules explored. We present our proposed model based on association rule mining in secure software development with the goal of building this ITS. Our proposed model and prototype experiments are discussed in this paper along with challenges and ongoing work.

The intelligent connected vehicle industry has entered a period of opportunity, industry data is accumulating rapidly, and the formulation of industry standards to regulate big data management and application is imminent. As the basis of data security, data classification has received unprecedented attention. By combing through the research and development status of data classification in various industries, this article combines industry characteristics and re-examines the framework of industry data classification from the aspects of information security and data assetization, and tries to find the balance point between data security and data value. The intelligent networked automobile industry provides support for big data applications, this article combines the characteristics of the connected vehicle industry, re-examines the data characteristics of the intelligent connected vehicle industry from the 2 aspects as information security and data assetization, and eventually proposes a scene-based hierarchical framework. The framework includes the complete classification process, model, and quantifiable parameters, which provides a solution and theoretical endorsement for the construction of a big data automatic classification system for the intelligent connected vehicle industry and safe data open applications.

Application of the attention model hybrid building based on the artificial intelligence in the course of the intelligent architecture history is studied in this article. A Hadoop distributed architecture using big data processing technology which combines basic building information with the building energy consumption data for the data mining research methods, and conduct a preliminary design of a Hadoop-based public building energy consumption data mining system. The principles of the proposed model were summarized. At first, the intelligent firewall processes the decision data faster, when the harmful information invades. The intelligent firewall can monitor and also intercept the harmful information in a timelier manner. Secondly, develop a problem data processing plan, delete and identify different types of problem data, and supplement the deleted problem data according to the rules obtained by data mining. The experimental results have reflected the efficiency of the proposed model.

The construction of power Internet of things is an important development direction of power grid enterprises in the future. Big data not only brings economic and social benefits to the power system industry, but also brings many information security problems. Therefore, in the case of accelerating the construction of ubiquitous electric Internet of things, it is urgent to standardize the data security protection in the ubiquitous electric Internet of things environment. By analyzing the characteristics of big data in power system, this paper discusses the security risks faced by big data in power system. Finally, we propose some methods of data security protection based on the defects of big data security in current power system. By building a data security intelligent management and control platform, it can automatically discover and identify the types and levels of data assets, and build a classification and grading information base of dynamic data assets. And through the detection and identification of data labels and data content characteristics, tracking the use of data flow process. So as to realize the monitoring of data security state. By protecting sensitive data against leakage based on the whole life cycle of data, the big data security of power grid informatization can be effectively guaranteed and the safety immunity of power information system can be improved.

In a block channel IoT system, sensitive details can be leaked by means of the proof of work or address check, as data or application Validation data is applied on the blockchain. In this, the zero-knowledge evidence is applied to a smart metering system to show how to improve the anonymity of the blockchain for privacy safety without disclosing information as a public key. Within this article, a blockchain has been implemented to deter security risks such as data counterfeiting by utilizing intelligent meters. Zero-Knowledge Proof, an anonymity blockchain technology, has been implemented through block inquiry to prevent threats to security like personal information infringement. It was suggested that intelligent contracts would be used to avoid falsification of intelligent meter data and abuse of personal details.

In article the basic criteria of quality of work of the automated control system of technological process (APCS) are considered, the analysis of critical moments and level of information safety of APCS is spent. The model of maintenance of information safety of APCS on the basis of technology of predictive maintenance with application of intellectual methods of data processing is offered. The model allows to generate the list of actions at detection of new kinds of the threats connected with destructive influences on object, proceeding from acceptability of predicted consequences of work of APCS. In article with use of the system analysis the complex model of the technical object of automation is developed, allowing to estimate consequences from realization of threats of information safety at various system levels of APCS.

Intelligent connected vehicle cyber security has attracted widespread attention this year. The safety of GNSS information is related to the safety of cars and has become a key technology. This paper researches the cyber security characteristics of intelligent connected vehicle navigation and positioning by analyzing the signal receiving mode of navigation and positioning on the vehicle terminal. The article expounds the principles of deceiving and interfering cyber security that lead to the safety of GNSS information. This paper studies the key causes of cyber security. Based on key causes, the article constructs a GNSS cyber security test method by combining a navigation signal simulator and an interference signal generator. The results shows that the method can realize the security test of the GNSS information of the vehicle terminal. This method provides a test method for the navigation terminal defense cyber security capability for a vehicle terminal, and fills a gap in the industry for the vehicle terminal information security test.