The introductory part of the research mainly focuses on the importance of using block chain facilities by using the 5G Network that can be useful for data privacy and security. It can be said that the research mainly focuses on all the benefits of using block chain technology in order to protect all the access of relevant data by implementing intelligent contracts for enhancing the security framework related to the use of 5G networks on the data protection activities. The Literature review of the research mainly concentrates on the benefits and merits of applying the block chain facilities for enhancing both the growth as well as the development of data protection and data privacy. All the merits, as well as demerits of using the block chain facility, have been also discussed throughout the overall research paper. On the other hand, various methods, as well as strategies for applying the block chain facilities, also have been analyzed throughout the literature review section of this research paper. A survey was conducted in this particular scenario to get a clear comprehension of the situation. A survey was conducted with fifty one random people that enable the researches to get a clear picture of the trend while fetching some real life data in this particular scenario.

The data of the government and enterprises, as the production factors are facing risks and problems of security violations, such as data leakage, data abuse and data tampering during quick circulation. This paper studies the security supervision architecture of data circulation (exchange, sharing, transaction) from the perspective of the whole life cycle, proposes and constructs the security supervision metadata model, which is used to represent the changes of users, behavior, data lineage, etc. during the whole life cycle of data; For massive data, based on the metadata model of security supervision, innovates the key technologies such as data security monitoring, tracing and ownership authentication; Per the verification need, a set of security supervision prototype showing security situation, tracing performance, ownership construction/authentication and low-level visual explorer is developed.

This paper explores the advantages and limitations of probabilistic and deterministic encryption schemes for securing sensitive data. While probabilistic encryption ensures high security for data encryption, it can pose limitations when filtering and querying data. On the other hand, deterministic encryption method is a more flexible and unchanging encryption scheme that allows for the benefits of filtering data while icing its security. Many platform encryptions use deterministic encryption to allow for filtering of translated data while minimizing exposure of plain values to cipher values. Still, deterministic encryption can still pose certain pitfalls and may reveal information to eavesdroppers. A promising variation of encryption for perfecting security in communication end is ‘Varying encryption’ which is grounded on factors such as distance and country of connection. This acclimatized approach offers increased speed and security and can confuse attackers, making it harder for them to gain access to information being transmitted. Though, careful analysis of the advantages and disadvantages of assigning a specific encryption standard to a given set of conditions is essential to achieve optimal results.

Connected, Cooperative, and Autonomous Mobility (CCAM) will take intelligent transportation to a new level of complexity. CCAM systems can be thought of as complex Systems-of-Systems (SoSs). They pose new challenges to security as consequences of vulnerabilities or attacks become much harder to assess. In this paper, we propose the use of a specific type of a trust model, called subjective trust network, to model and assess trustworthiness of data and nodes in an automotive SoS. Given the complexity of the topic, we illustrate the application of subjective trust networks on a specific example, namely Cooperative Intersection Management (CIM). To this end, we introduce the CIM use-case and show how it can be modelled as a subjective trust network. We then analyze how such trust models can be useful both for design time and run-time analysis, and how they would allow us a more precise quantitative assessment of trust in automotive SoSs. Finally, we also discuss the open research problems and practical challenges that need to be addressed before such trust models can be applied in practice.

IBMD(Intelligent Behavior-Based Malware Detection) aims to detect and mitigate malicious activities in cloud computing environments by analyzing the behavior of cloud resources, such as virtual machines, containers, and applications.The system uses different machine learning methods like deep learning and artificial neural networks, to analyze the behavior of cloud resources and detect anomalies that may indicate malicious activity. The IBMD system can also monitor and accumulate the data from various resources, such as network traffic and system logs, to provide a comprehensive view of the behavior of cloud resources. IBMD is designed to operate in a cloud computing environment, taking advantage of the scalability and flexibility of the cloud to detect malware and respond to security incidents. The system can also be integrated with existing security tools and services, such as firewalls and intrusion detection systems, to provide a comprehensive security solution for cloud computing environments.

Named Data Networking (NDN) has been considered a promising network architecture for Vehicular Ad Hoc Networks (VANETs), what became known as Vehicular Named-Data Networking (VNDN). This new paradigm brings the potential to improve Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) that are inefficient in urban intelligent transport scenarios. Despite the advantages, VNDN brings inherent problems, such as the routing interest packages on NDN, which causes serious problem in the vehicular environment. The broadcast storm attack results in a huge amount of packet loss, provoking transmission overload. In addition, the link disconnection caused by the highly dynamic topology leads to a low package delivery rate. In this article, we propose a strategy for forwarding packages of interest in VNDN networks, using fuzzy logic to mitigate the broadcast storm. The proposal also aims to avoid packet collision and efficient data recovery, which the approach is based on metrics such as the nodes distance, the link stability and the signal quality. The results show a reduction in the number of Interest and Data packets without disrupting network performance maintaining adequate Interest delays.

Entering the critical year of the 14th Five Year Plan, China s information security industry has entered a new stage of development. With the increasing importance of information security, its industrial development has been paid attention to, but the data fragmentation of China s information security industry is serious, and there are few corresponding summaries and predictions. To achieve the development prediction of the industry, this article studies the intelligent prediction of information security industry data based on machine learning and new adaptive weighted fusion, and deduces the system based on the research results to promote industry development. Firstly, collect, filter, integrate, and preprocess industry data. Based on the characteristics of the data, machine learning algorithms such as linear regression, ridge regression, logical regression, polynomial regression and random forest are selected to predict the data, and the corresponding optimal parameters are found and set in the model creation. And an improved adaptive weighted fusion model based on model prediction performance was proposed. Its principle is to adaptively select the model with the lowest mean square error (MSE) value for fusion based on the real-time prediction performance of multiple machine learning models, and its weight is also calculated adaptively to improve prediction accuracy. Secondly, using technologies such as Matplotlib and Pyecharts to visualize the data and predicted results, it was found that the development trend of the information security industry is closely related to factors such as national information security laws and regulations, the situation between countries, and social emergencies. According to the predicted results of the data, it is observed that both industry input and output have shown an upward trend in recent years. In the future, China s information security industry is expected to maintain stable and rapid growth driven by the domestic market.

The last decade witnessed a gradual shift from cloudbased computing towards ubiquitous computing, which has put at a greater security risk every element of the computing ecosystem including devices, data, network, and decision making. Indeed, emerging pervasive computing paradigms have introduced an uncharted territory of security vulnerabilities and a wider attack surface, mainly due to network openness, the underlying mechanics that enable intelligent functions, and the deeply integrated physical and cyber spaces. Furthermore, interconnected computing environments now enjoy many unconventional characteristics that mandate a radical change in security engineering tools. This need is further exacerbated by the rapid emergence of new Advanced Persistent Threats (APTs) that target critical infrastructures and aim to stealthily undermine their operations in innovative and intelligent ways. To enable system and network designers to be prepared to face this new wave of dangerous threats, this paper overviews recent APTs in emerging computing systems and proposes a new approach to APTs that is more tailored towards such systems compared to traditional IT infrastructures. The proposed APT lifecycle will inform security decisions and implementation choices in future pervasive networked systems.

Traditional Web application category recognition is implemented by fingerprint rule matching, which is difficult to extract fingerprint rules and has limited coverage. At present, many improved identification methods semi-automatically extract fingerprints through certain rules and identify Web application categories through clustering or classification algorithms, but still rely on fingerprint rules and human intervention, and the time complexity of classification is too high to process a large amount of data. This paper proposes Multi-layer Simhash Algorithm and combines DBSCAN clustering to realize intelligent identification of Web application types, pioneering the complete automation of fingerprint identification of Web applications. This method has the function of discovering unknown Web applications and predicting unknown application types, and solves the problems of fingerprint rule extraction and manual dependence of Web applications. This paper through the TF-IDF algorithm to extract the Web page text key words and weight, Then, Multi-layer Simhash Algorithm is used to transform text feature words and weights into binary characteristic hash value, at last, the hamming distance between the input Web page and the characteristic hash value of the known category is compared with the radius of the base class, which determines the category of the input Web application. The experimental results show that the accuracy of Web application category recognition and prediction is more than 97\% and 93\% respectively.

By analyzing the design requirements of a secure desktop virtualization information system, this paper proposes the security virtualization technology of "whitelist" security mechanism, the virtualization layer security technology of optimized design, and the virtual machine security technology of resource and network layer isolation. On this basis, this paper constructs the overall architecture of the secure desktop virtualization information system. This paper studies the desktop virtualization technology research based on VMware using VMware server virtualization solution to transform and upgrade the traditional intelligent desktop virtualization system, improve server resource utilization rate, and reduce operation and maintenance costs.

In recent years, in order to continuously promote the construction of safe cities, security monitoring equipment has been widely used all over the country. How to use computer vision technology to realize effective intelligent analysis of violence in video surveillance is very important to maintain social stability and ensure people s life and property safety. Video surveillance system has been widely used because of its intuitive and convenient advantages. However, the existing video monitoring system has relatively single function, and generally only has the functions of monitoring video viewing, query and playback. In addition, relevant researchers pay less attention to the complex abnormal behavior of violence, and relevant research often ignores the differences between violent behaviors in different scenes. At present, there are two main problems in video abnormal behavior event detection: the video data of abnormal behavior is less and the definition of abnormal behavior in different scenes cannot be clearly distinguished. The main existing methods are to model normal behavior events first, and then define videos that do not conform to the normal model as abnormal, among which the learning method of video space-time feature representation based on deep learning shows a good prospect. In the face of massive surveillance videos, it is necessary to use deep learning to identify violent behaviors, so that the machine can learn to identify human actions, instead of manually monitoring camera images to complete the alarm of violent behaviors. Network training mainly uses video data set to identify network training.

Wearables Security 2022 - One of the biggest new trends in artificial intelligence is the ability to recognise people s movements and take their actions into account. It can be used in a variety of ways, including for surveillance, security, human-computer interaction, and content-based video retrieval. There have been a number of researchers that have presented vision-based techniques to human activity recognition. Several challenges need to be addressed in the creation of a vision-based human activity recognition system, including illumination variations in human activity recognition, interclass similarity between scenes, the environment and recording setting, and temporal variation. To overcome the above mentioned problem, by capturing or sensing human actions with help of wearable sensors, wearable devices, or IoT devices. Sensor data, particularly one-dimensional time series data, are used in the work of human activity recognition. Using 1D-Convolutional Neural Network (CNN) models, this works aims to propose a new approach for identifying human activities. The Wireless Sensor Data Mining (WISDM) dataset is utilised to train and test the 1D-CNN model in this dissertation. The proposed HAR-CNN model has a 95.2\%of accuracy, which is far higher than that of conventional methods.

Science of Security 2022 - 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.

Object Oriented Security - At present, the traditional substation auxiliary control system is faced with the following four problems: poor real-time capability to abnormal response, high dependence on people when solving malfunctions, the communication, deployment and expansion of different underlying devices, and the lack of security mechanism. To solve these problems or optimize the functions, an intelligent substation auxiliary control system is proposed. The system innovatively applies OPC UA to the construction of the auxiliary control system. First, through the use of OPC UA s unique object-oriented modeling method as well as the joint specification modeling of OPC UA and IEC61850, to solve the data communication problems caused by heterogeneous devices. Second, applying the Client/Server mode to realize the remote access from authorized mobile clients and give instructions, to cope with abnormal conditions, which reduces the dependency on people. Clients of other authorized enterprises are allowed to access the working data of the devices they are interested in, makes full use of massive data and ensures the information security of the system. Third, Pub/Sub mode is applied to enable the underlying devices to communicate directly with each other through the middleware, which reduces the response time of equipment joint debugging and improve the real-time performance. In addition, through OPC UA, the industrial data of the system can be transmitted over the Internet, realizing the combination of the Internet of Things and the Internet, which is an idea of the combination of the two in the future.

Network Security Architecture - 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.

Malware Classification - Methodologies used for the detection of malicious applications can be broadly classified into static and dynamic analysis based approaches. With traditional signature-based methods, new variants of malware families cannot be detected. A combination of deep learning techniques along with image-based features is used in this work to classify malware. The data set used here is the ‘Malimg’ dataset, which contains a pictorial representation of well-known malware families. This paper proposes a methodology for identifying malware images and classifying them into various families. The classification is based on image features. The features are extracted using the pre-trained model namely VGG16. The samples of malware are depicted as byteplot grayscale images. Features are extracted employing the convolutional layer of a VGG16 deep learning network, which uses ImageNet dataset for the pre-training step. The features are used to train different classifiers which employ SVM, XGBoost, DNN and Random Forest for the classification task into different malware families. Using 9339 samples from 25 different malware families, we performed experimental evaluations and demonstrate that our approach is effective in identifying malware families with high accuracy.

Malware Analysis and Graph Theory - 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.

Internet-scale Computing Security - The scale of the intelligent networked vehicle market is expanding rapidly, and network security issues also follow. A Situational Awareness (SA) system can detect, identify, and respond to security risks from a global perspective. In view of the discrete and weak correlation characteristics of perceptual data, this paper uses the Fly Optimization Algorithm (FOA) based on dynamic adjustment of the optimization step size to improve the convergence speed, and optimizes the extraction model of security situation element of the Internet of Vehicles (IoV), based on Probabilistic Neural Network (PNN), to improve the accuracy of element extraction. Through the comparison of experimental algorithms, it is verified that the algorithm has fast convergence speed, high precision and good stability.

Internet-scale Computing Security - The big data platform based on cloud computing realizes the storage, analysis and processing of massive data, and provides users with more efficient, accurate and intelligent Internet services. Combined with the characteristics of college teaching resource sharing platform based on cloud computing mode, the multi-faceted security defense strategy of the platform is studied from security management, security inspection and technical means. In the detection module, the optimization of the support vector machine is realized, the detection period is determined, the DDoS data traffic characteristics are extracted, and the source ID blacklist is established; the triggering of the defense mechanism in the defense module, the construction of the forwarder forwarding queue and the forwarder forwarding capability are realized. Reallocation.

Internet of Vehicles Security - With the development of urbanization, the number of vehicles is gradually increasing, and vehicles are gradually developing in the direction of intelligence. How to ensure that the data of intelligent vehicles is not tampered in the process of transmission to the cloud is the key problem of current research. Therefore, we have established a data security transmission system based on blockchain. First, we collect and filter vehicle data locally, and then use blockchain technology to transmit key data. Through the smart contract, the key data is automatically and accurately transmitted to the surrounding node vehicles, and the vehicles transmit data to each other to form a transaction and spread to the whole network. The node data is verified through the node data consensus protocol of intelligent vehicle data security transmission system, and written into the block to form a blockchain. Finally, the vehicle user can query the transaction record through the vehicle address. The results show that we can safely and accurately transmit and query vehicle data in the blockchain database.

Internet of Vehicles 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.

Internet of Vehicles Security - The scale of the intelligent networked vehicle market is expanding rapidly, and network security issues also follow. A Situational Awareness (SA) system can detect, identify, and respond to security risks from a global perspective. In view of the discrete and weak correlation characteristics of perceptual data, this paper uses the Fly Optimization Algorithm (FOA) based on dynamic adjustment of the optimization step size to improve the convergence speed, and optimizes the extraction model of security situation element of the Internet of Vehicles (IoV), based on Probabilistic Neural Network (PNN), to improve the accuracy of element extraction. Through the comparison of experimental algorithms, it is verified that the algorithm has fast convergence speed, high precision and good stability.

Intelligent Data and Security - 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 Data and Security - 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.

Intelligent Data and Security - 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.