Intelligent Data and Security - 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.

Intelligent Data and Security - As a new industry integrated by computing, communication, networking, electronics, and automation technology, the Internet of Vehicles (IoV) has been widely concerned and highly valued at home and abroad. With the rapid growth of the number of intelligent connected vehicles, the data security risks of the IoV have become increasingly prominent, and various attacks on data security emerge in an endless stream. This paper firstly introduces the latest progress on the data security policies, regulations, standards, technical routes in major countries and regions, and international standardization organizations. Secondly, the characteristics of the IoV data are comprehensively analyzed in terms of quantity, standard, timeliness, type, and cross-border transmission. Based on the characteristics, this paper elaborates the security risks such as privacy data disclosure, inadequate access control, lack of identity authentication, transmission design defects, cross-border flow security risks, excessive collection and abuse, source identification, and blame determination. And finally, we put forward the measures and suggestions for the security development of IoV data in China.

Intelligent Data and Security - 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.

Intelligent Data and Security - 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.

Intelligent Data and Security - 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.

Intelligent Data and Security - 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.

Intelligent Data and Security - 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.

Industrial Control Systems - With the introduction of the national “carbon peaking and carbon neutrality” strategic goals and the accelerated construction of the new generation of power systems, cloud applications built on advanced IT technologies play an increasingly important role in meeting the needs of digital power business. In view of the characteristics of the current power industrial control system operation support cloud platform with wide coverage, large amount of log data, and low analysis intelligence, this paper proposes a cloud platform network security behavior audit method based on FP-Growth association rule algorithm, aiming at the uniqueness of the operating data of the cloud platform that directly interacts with the isolated system environment of power industrial control system. By using the association rule algorithm to associate and classify user behaviors, our scheme formulates abnormal behavior judgment standards, establishes an automated audit strategy knowledge base, and improves the security audit efficiency of power industrial control system operation support cloud platform. The intelligent level of log data analysis enables effective discovery, traceability and management of internal personnel operational risks.

Nowadays, the popularity of intelligent terminals makes malwares more and more serious. Among the many features of application, the call graph can accurately express the behavior of the application. The rapid development of graph neural network in recent years provides a new solution for the malicious analysis of application using call graphs as features. However, there are still problems such as low accuracy. This paper established a large-scale data set containing more than 40,000 samples and selected the class call graph, which was extracted from the application, as the feature and used the graph embedding combined with the deep neural network to detect the malware. The experimental results show that the accuracy of the detection model proposed in this paper is 97.7\%; the precision is 96.6\%; the recall is 96.8\%; the F1-score is 96.4\%, which is better than the existing detection model based on Markov chain and graph embedding detection model.

The security of Energy Data collection is the basis of achieving reliability and security intelligent of smart grid. The newest security communication of Data collection is Zero Trust communication; The Strategy of Zero Trust communication is that don’t trust any device of outside or inside. Only that device authenticate is successful and software and hardware is more security, the Energy intelligent power system allow the device enroll into network system, otherwise deny these devices. When the device has been communicating with the Energy system, the Zero Trust still need to detect its security and vulnerability, if device have any security issue or vulnerability issue, the Zero Trust deny from network system, it ensures that Energy power system absolute security, which lays a foundation for the security analysis of intelligent power unit.

The Internet of Things (IoT) is a technology that has evolved to make day-to-day life faster and easier. But with the increase in the number of users, the IoT network is prone to various security and privacy issues. And most of these issues/attacks occur during the routing of the data in the IoT network. Therefore, for secure routing among resource-constrained nodes of IoT, the RPL protocol has been standardized by IETF. But the RPL protocol is also vulnerable to attacks based on resources, topology formation and traffic flow between nodes. The attacks like DoS, Blackhole, eavesdropping, flood attacks and so on cannot be efficiently defended using RPL protocol for routing data in IoT networks. So, defense mechanisms are used to protect networks from routing attacks. And are classified into Secure Routing Protocols (SRPs) and Intrusion Detection systems (IDs). This paper gives an overview of the RPL attacks and the defense mechanisms used to detect or mitigate the RPL routing attacks in IoT networks.

The development of industrial robots, as a carrier of artificial intelligence, has played an important role in promoting the popularisation of artificial intelligence super automation technology. The paper introduces the system structure, hardware structure, and software system of the mobile robot climber based on computer big data technology, based on this research background. At the same time, the paper focuses on the climber robot's mechanism compound method and obstacle avoidance control algorithm. Smart home computing focuses on “home” and brings together related peripheral industries to promote smart home services such as smart appliances, home entertainment, home health care, and security monitoring in order to create a safe, secure, energy-efficient, sustainable, and comfortable residential living environment. It's been twenty years. There is still no clear definition of “intelligence at home,” according to Philips Inc., a leading consumer electronics manufacturer, which once stated that intelligence should comprise sensing, connectedness, learning, adaption, and ease of interaction. S mart applications and services are still in the early stages of development, and not all of them can yet exhibit these five intelligent traits.

Transformer is the key equipment of power system, and its stable operation is very important to the security of power system In practical application, with the progress of technology, the performance of transformer becomes more and more important, but faults also occur from time to time in practical application, and the traditional manual fault diagnosis needs to consume a lot of time and energy. At present, the rapid development of artificial intelligence technology provides a new research direction for timely and accurate detection and treatment of transformer faults. In this paper, a method of transformer fault diagnosis using artificial neural network is proposed. The neural network algorithm is used for off-line learning and training of the operation state data of normal and fault states. By adjusting the relationship between neuron nodes, the mapping relationship between fault characteristics and fault location is established by using network layer learning, Finally, the reasoning process from fault feature to fault location is realized to realize intelligent fault diagnosis.

Machine learning (ML) has been applied in prognostics and health management (PHM) to monitor and predict the health of industrial machinery. The use of PHM in production systems creates a cyber-physical, omni-layer system. While ML offers statistical improvements over previous methods, and brings statistical models to bear on new systems and PHM tasks, it is susceptible to performance degradation when the behavior of the systems that ML is receiving its inputs from changes. Natural changes such as physical wear and engineered changes such as maintenance and rebuild procedures are catalysts for performance degradation, and are both inherent to production systems. Drawing from data on the impact of maintenance procedures on ML performance in hydraulic actuators, this paper presents a simulation study that investigates how long it takes for ML performance degradation to create a difference in the throughput of serial production system. In particular, this investigation considers the performance of an ML model learned on data collected before a rebuild procedure is conducted on a hydraulic actuator and an ML model transfer learned on data collected after the rebuild procedure. Transfer learning is able to mitigate performance degradation, but there is still a significant impact on throughput. The conclusion is drawn that ML faults can have drastic, non-linear effects on the throughput of production systems.

Cyber Physical Systems (CPS), which contain devices to aid with physical infrastructure activities, comprise sensors, actuators, control units, and physical objects. CPS sends messages to physical devices to carry out computational operations. CPS mainly deals with the interplay among cyber and physical environments. The real-time network data acquired and collected in physical space is stored there, and the connection becomes sophisticated. CPS incorporates cyber and physical technologies at all phases. Cyber Physical Systems are a crucial component of Internet of Things (IoT) technology. The CPS is a traditional concept that brings together the physical and digital worlds inhabit. Nevertheless, CPS has several difficulties that are likely to jeopardise our lives immediately, while the CPS's numerous levels are all tied to an immediate threat, therefore necessitating a look at CPS security. Due to the inclusion of IoT devices in a wide variety of applications, the security and privacy of users are key considerations. The rising level of cyber threats has left current security and privacy procedures insufficient. As a result, hackers can treat every person on the Internet as a product. Deep Learning (DL) methods are therefore utilised to provide accurate outputs from big complex databases where the outputs generated can be used to forecast and discover vulnerabilities in IoT systems that handles medical data. Cyber-physical systems need anomaly detection to be secure. However, the rising sophistication of CPSs and more complex attacks means that typical anomaly detection approaches are unsuitable for addressing these difficulties since they are simply overwhelmed by the volume of data and the necessity for domain-specific knowledge. The various attacks like DoS, DDoS need to be avoided that impact the network performance. In this paper, an effective Network Cluster Reliability Model with enhanced security and privacy levels for the data in IoT for Anomaly Detection (NSRM-AD) using deep learning model is proposed. The security levels of the proposed model are contrasted with the proposed model and the results represent that the proposed model performance is accurate

Security and privacy are one of crucial factor in the area of information technology and iys applications. Ad-hoc network is a type of non-infrastructure wireless network that is more prone to be attacked and abused due to its properties. Deploying the ad-hoc network in vehicular environment needs the additional security consideration to prevent the attacks that can cause the serious harms like accidents, crashes and fatality of living being lives. In this paper we have explored analysis and requirements of the security solution for the ad hoc network under the vehicular environment. Different categories of threats, their risks are evaluated and then various issues related to deploying the security solutions are addressed by mentioning the proper security technologies and tools.

Vehicle Ad-Hoc Networks (VANETs) are a special type of Mobile Ad-Hoc Network (MANETs). In VANETs, a group of vehicles communicates with each other to transfer data without a need for a fixed infrastructure. In this paper, we compare the performance of two routing protocols: Ad-hoc on Demand Distance Vector protocol (AODV) and Destination-Sequenced Distance Vector protocol (DSDV) in VANETs. We measure the reliability of each protocol in the packet delivery.

Vehicular Ad hoc Network (VANET) is an emerging technology that is used to provide communication between vehicle users. VANET provides communication between one vehicle node to another vehicle node, vehicle to the roadside unit, vehicle to pedestrian, and even vehicle to rail users. Communication between nodes should be very secure and confidential, Since VANET communicates through wireless mode, a malicious node may enter inside the communication zone to hack, inject false messages, and interrupt the communication. A strong protocol is necessary to detect malicious nodes and authenticate the VANET user to protect them from malicious attacks. In this paper, a fuzzy-based trust authentication scheme is used to detect malicious nodes with the Mamdani fuzzy Inference system. The parameter estimation, rules have been framed using MATLAB Mamdani Fuzzy Inference system to select a genuine node for data transmission.

Vehicular Ad-hoc Networks (VANETs) is a very fast emerging research area these days due to their contribution in designing Intelligent transportation systems (ITS). ITS is a well-organized group of wireless networks. It is a derived class of Mobile Ad-hoc Networks (MANETs). VANET is an instant-formed ad-hoc network, due to the mobility of vehicles on the road. The goal of using ITS is to enhance road safety, driving comfort, and traffic effectiveness by alerting the drivers at right time about upcoming dangerous situations, traffic jams, road diverted, weather conditions, real-time news, and entertainment. We can consider Vehicular communication as an enabler for future driverless cars. For these all above applications, it is necessary to make a threat-free environment to establish secure, fast, and efficient communication in VANETs. In this paper, we had discussed the overviews, characteristics, securities, applications, and various data dissemination techniques in VANET.

The Sixth Generation (6G) is currently under development and it is a planned successor of the Fifth Generation (5G). It is a new wireless communication technology expected to have a greater coverage area, significant fast and a higher data rate. The aim of this paper is to examine the literature on challenges and possible solutions of 6G's security, privacy and trust. It uses the systematic literature review technique by searching five research databases for search engines which are precise keywords like “6G,” “6G Wireless communication,” and “sixth generation”. The latter produced a total of 1856 papers, then the security, privacy and trust issues of the 6G wireless communication were extracted. Two security issues, the artificial intelligence and visible light communication, were apparent. In conclusion, there is a need for new paradigms that will provide a clear 6G security solutions.

The popularity of portable web browsers is increasing due to its convenient and compact nature along with the benefit of the data being stored and transferred easily using a USB drive. As technology gets updated frequently, developers are working on web browsers that can be portable in nature with additional security features like private mode browsing, built in ad blockers etc. The increased probability of using portable web browsers for carrying out nefarious activities is a result of cybercriminals with the thought that if they use portable web browsers in private mode it won't leave a digital footprint. Hence, the research paper aims at performing a comparative study of four portable web browsers namely Brave, TOR, Vivaldi, and Maxthon along with various memory acquisition tools to understand the quantity and quality of the data that can be recovered from the memory dump in two different conditions that is when the browser tabs were open and when the browser tabs were closed in a system to aid the forensic investigators.

Design of smart risk assessment system for the agricultural products and the food safety inspection based on multivariate data analysis is studied in this paper. The designed quality traceability system also requires the collaboration and cooperation of various companies in the supply chain, and a unified database, including agricultural product identification system, code system and security status system, is required to record in detail the trajectory and status of agricultural products in the logistics chain. For the improvement, the multivariate data analysis is combined. Hadoop cannot be used on hardware with high price and high reliability. Even for groups with high probability of the problems, HDFS will continue to use when facing problems, and at the same time. Hence, the core model of HDFS is applied into the system. In the verification part, the analytic performance is simulated.

Machine Learning (ML) and Artificial Intelligence (AI) techniques are widely adopted in the telecommunication industry, especially to automate beyond 5G networks. Federated Learning (FL) recently emerged as a distributed ML approach that enables localized model training to keep data decentralized to ensure data privacy. In this paper, we identify the applicability of FL for securing future networks and its limitations due to the vulnerability to poisoning attacks. First, we investigate the shortcomings of state-of-the-art security algorithms for FL and perform an attack to circumvent FoolsGold algorithm, which is known as one of the most promising defense techniques currently available. The attack is launched with the addition of intelligent noise at the poisonous model updates. Then we propose a more sophisticated defense strategy, a threshold-based clustering mechanism to complement FoolsGold. Moreover, we provide a comprehensive analysis of the impact of the attack scenario and the performance of the defense mechanism.

As a new industry integrated by computing, communication, networking, electronics, and automation technology, the Internet of Vehicles (IoV) has been widely concerned and highly valued at home and abroad. With the rapid growth of the number of intelligent connected vehicles, the data security risks of the IoV have become increasingly prominent, and various attacks on data security emerge in an endless stream. This paper firstly introduces the latest progress on the data security policies, regulations, standards, technical routes in major countries and regions, and international standardization organizations. Secondly, the characteristics of the IoV data are comprehensively analyzed in terms of quantity, standard, timeliness, type, and cross-border transmission. Based on the characteristics, this paper elaborates the security risks such as privacy data disclosure, inadequate access control, lack of identity authentication, transmission design defects, cross-border flow security risks, excessive collection and abuse, source identification, and blame determination. And finally, we put forward the measures and suggestions for the security development of IoV data in China.

Design a new generation of smart power meter components, build a smart power network, implement power meter safety protection, and complete smart power meter network security protection. The new generation of smart electric energy meters mainly complete legal measurement, safety fee control, communication, control, calculation, monitoring, etc. The smart power utilization structure network consists of the master station server, front-end processor, cryptographic machine and master station to form a master station management system. Through data collection and analysis, the establishment of intelligent energy dispatching operation, provides effective energy-saving policy algorithms and strategies, and realizes energy-smart electricity use manage. The safety protection architecture of the electric energy meter is designed from the aspects of its own safety, full-scenario application safety, and safety management. Own security protection consists of hardware security protection and software security protection. The full-scene application security protection system includes four parts: boundary security, data security, password security, and security monitoring. Security management mainly provides application security management strategies and security responsibility division strategies. The construction of the intelligent electric energy meter network system lays the foundation for network security protection.