Network Security Control Research Articles

Design and implementation of computer network security detection and control system

With the widespread application of computer network technology, computer network security defense capability has become a focus of attention in various industries. The extensive use of computer information systems in various sectors has significantly improved work efficiency but has also introduced security risks and management issues. The deployment of network security detection and control systems allows for effective security monitoring and management of computer operations and real-time network information. This paper presents a computer network security detection and control system based on human-computer interaction, which enables users to handle daily key business processes. The work principles and overall architecture of the network security detection and control system are analyzed and demonstrated. It offers functions such as filtering options, address rules, network security detection, network unreachability, overall traffic analysis, subnet definition, fault diagnosis, and security analysis. Testing and analysis indicate that the systems design achieves the intended goals. In the application of network security features, it can effectively combine various forces to enhance the quality and efficiency of network security, making it widely applicable in various industry units.

Evaluation of Internet of Things computer network security and remote control technology

Evaluation of Internet of Things computer network security and remote control technology

Retracted: Sensor Network Security Risk Prediction and Control Method Based on Big Data Analysis

Retracted: Sensor Network Security Risk Prediction and Control Method Based on Big Data Analysis

Malicious DNS Tunnel Tool Recognition Using Persistent DoH Traffic Analysis

DNS over HTTPS (DoH) protocol can mitigate the risk of privacy breaches but makes it difficult to control network security services due to the DNS traffic encryption. However, since malicious DNS tunnel tools for the DoH protocol pose network security threats, network administrators need to recognize malicious communications even after the DNS traffic encryption has become widespread. In this paper, we propose a malicious DNS tunnel tool recognition system using persistent DoH traffic analysis based on machine learning. The proposed system can accomplish continuous knowledge updates for emerging malicious DNS tunnel tools on the machine learning model. The system is based on hierarchical machine learning classification and focuses on DoH traffic analysis. The evaluation results confirm that the proposed system is able to recognize the six malicious DNS tunnel tools in total, not only well-known ones, including dns2tcp, dnscat2, and iodine, but also the emerging ones such as dnstt, tcp-over-dns, and tuns with 98.02% classification accuracy.

Voltage Optimization Control and Risk Assessment Technology for High-proportion Distributed Photovoltaic Access to Distribution Network

The high proportion of distributed photovoltaic connected to the distribution network will lead to an increase in terminal voltage and network loss, which will not only generate many adverse effects on the safe and reliable operation of the distribution network but also easily lead to power supply disconnection and power supply interruption accidents caused by voltage out of limit. At present, the voltage control of the distribution network does not fully consider the regulation characteristics of the photovoltaic inverter itself and ignores the impact of different control methods on the distribution network security risk management and control. Therefore, this paper establishes a voltage closed-loop control model for high-proportion distributed photovoltaic access to the distribution network and proposes a real-time active voltage optimization control strategy with the goal of minimizing the deviation of the actual operation state of the distribution network from the scheduling plan. Finally, a typical industrial park is taken as a case for calculation and analysis. The results show that using real-time data to formulate a real-time voltage optimization control strategy can not only ensure the economic operation of each voltage regulating unit in the distribution network but also reduce operational risks. The control method proposed in this paper provides a new idea for improving the voltage control level of distributed photovoltaic access distribution network and fully guarantees the operation reliability of the entire distribution network.

The Institutionalisation of Information Security Management Practices in selected Organisations in Uganda

The study aimed at examining the extent to which information security management practices were institutionalised in corporate organisations. Evidence shows that failure by organisations to entrench the information security management practices (ISMPs) into organisations’ structures opens the gateway for attacks, threat actors and information breaches to cause harm to information assets with ease. The study explored the phenomenon in its social setting hence the adoption of descriptive research design as the research methodology. The institutional theory was adopted as a new dimension in examining information security management in organisations. This theory suggests that control gears like coercive, normative, mimetic and management commitment could be used to effectively entrench security guidelines in organisations. Methodical scrutiny of the institutionalisation process: development, implementation and maintenance, and evaluation were also carried out. The researcher relied on human experience to make sense of the institutionalised processes. Extant literature was reviewed, and survey questionnaires were developed based on the eleven ISMPs and administered to purposively selected respondents from the two organisations. The eleven ISMPs covered include state of information security policy, asset management, secure information sharing, supply chain security, access management, network security controls, portable and removable media security, remote access security, protective monitoring of information systems, implementation of information security back-ups, and security accreditation by professional bodies. Data analysis was done using SPSS. Findings indicate that organisations have not fully incorporated all the eleven ISMPs covered as best practices and standards. Based on the results from the field, answers to the research questions were partly realised. Recommendations like the implementation of ISMPs to check deficiencies identified, customisation of security guidelines to protect information assets and institutionalisation of security practices at all levels were suggested. Overall, the study was a positive step towards the institutionalisation process of ISMPs in organisations

A linear-quadratic mean-field game of backward stochastic differential equation with partial information and common noise

This paper studies a linear-quadratic (LQ) mean-field game of stochastic large-population system with partial information and common noise, where the large-population system satisfies a class of backward stochastic differential equations (BSDEs), and both coupling structure and ki (a part of solution of BSDE) enter state equations and cost functionals. By virtue of stochastic maximum principle and optimal filter technique, we obtain a Hamiltonian system first, which is a fully-coupled forward-backward stochastic differential equation (FBSDE). Decoupling the Hamiltonian system, we obtain three ordinary differential equations (ODEs), a forward and a backward optimal filtering equations, which enable us to derive an optimal control of an auxiliary limiting control problem. Next, we verify that a decentralized control strategy is an ϵ-Nash equilibrium of the LQ game. Finally, we solve a product pricing problem and a network security control problem in applications. We give a near-optimal price strategy and a near-optimal control strategy with numerical simulations, respectively.

An Analysis of Internet of Things Computer Network Security and Remote Control Technology

In order to solve the problem of the specific application of Internet of Things computer network security monitoring technology in infrastructure construction, a method of remote network monitoring system design is proposed, taking tunnel security construction as an example. According to the characteristics of construction tunnel safety monitoring, this method provides theoretical guidance from several aspects, such as computer remote monitoring, data decision analysis, safety control, personnel positioning, and data management. Through experiments, it is found that in the safety detection experiment, when the data is extracted within 2 minutes for detection, the detection accuracy is higher than 80%, and if it is 4 minutes, the detection accuracy is higher than 90%; in the response time test, the local control response time is generally less than 1 s. In the remote control response time test, the response time can generally be within 3 s, and it may be greater than 5 s. The designed construction tunnel monitoring system transmits the collected data to the monitoring terminal remotely. Then, the sensor data is analyzed for timing decision-making. This method can effectively calculate the safety status of each monitoring unit of the current tunnel according to a certain algorithm and formulate corresponding emergency measures according to the safety status.

Legal Management of Network Information Security Based on Embedded Real-Time Task Processing.

Nowadays, with the continuous advancement of globalization, Internet technology has made an unprecedented leap, which makes the amount of data on the Internet grow exponentially. Data are not only diverse and numerous, but also spread fast and of high value. Therefore, the Internet is entering the era of big data. However, the openness of information network itself, the immaturity of technology, and the imperfection of network security law make it vulnerable to network attacks, resulting in the disclosure of network users' information and the theft of their privacy data. The formulation of network information security law to protect network information security is of great significance for the development of multidomain network information security under the background of big data and ensuring users' online security. Firstly, this article introduces the basic information of embedded real-time task system, analyzes the factors of real-time Linux based on kernel preemption, interrupts mechanism and policy planning, and summarizes the process of some embedded real-time task processing. Integrating legal management, this article puts forward three elements of network security control mechanism, namely, network control personnel, environment, and technology. Taking these three elements as the starting point of the construction of information security control mechanism, this article provides the steps of using evaluation model to evaluate network information security. Finally, it identifies the problems existing in the legal management of network information security and formulates the corresponding strategies. By combining embedded technology and real-time task processing technology, this study applies it to the field of network information security legal management, to promote its development.

Research on Situational Awareness Technology of Industrial Control Network Based on Big Data

With the rapid advancement of national strategies such as "Internet+" and "Made in China 2025", industrial control systems have been widely used in various industries such as energy, municipalities, transportation, water conservancy and aerospace, etc. The security of industrial control networks is always affecting the lifeline of the national economy. Therefore, the security of industrial control networks has not been given enough attention, resulting in frequent industrial control network security incidents, which makes us realize the importance of creating an industrial control network situational awareness system that integrates assessment and prediction. In this paper, we study the situational awareness technology of industrial control network based on big data, and integrate situational extraction as the premise, situational assessment as the core, and situational prediction as the goal to comprehensively sense the situational awareness of industrial control network system. Firstly, we adopt two ways of industrial control network data collection in the situational extraction, one is the use of traffic mirroring bypass access to sensor-aware terminals, without affecting the original production services on the premise of network traffic data collection. The second is the use of WireShark tools to achieve industrial control network traffic packet collection and statistics of traffic packets per second, analysis of the current network state, build Hadoop big data platform to achieve offline data pre-processing and feature extraction, and build Flink and TensorFlow model for graph neural network model training and complete prediction. Secondly, we use a combination of hierarchical analysis and correlation analysis to evaluate the situation, and use the evaluation graph to show the current network security state. Thirdly, the situation prediction is done by training the graph neural network model offline. We use the Flink real-time computation engine to read the data of the industrial control network into the graph neural network model in real time, which is used to enhance the feature representation of each node and to predict the probability of anomaly occurrence of the industrial control network in the future period. Finally, the graph neural network model of this paper is compared with traditional neural networks and machine learning models, and the accuracy and false alarm rate indexes are used to demonstrate the high accuracy and robustness of this model.

How Do Socio-Demographic Patterns Define Digital Privacy Divide?

Digital privacy has become an essential component of information and communications technology (ICT) systems. There are many existing methods for digital privacy protection, including network security, cryptography, and access control. However, there is still a gap in the digital privacy protection levels available for users. This paper studies the digital privacy divide (DPD) problem in ICT systems. First, we introduce an online DPD study for understanding the DPD problem by collecting responses from 776 ICT users using crowdsourcing task assignments. Second, we propose a factor analysis-based statistical method for generating the DPD index from a set of observable DPD question variables. In particular, the DPD index provides one scaled measure for the DPD gap by exploring the dimensionality of the eight questions in the DPD survey. Third, we introduce a DPD proportional odds model for analyzing the relationship between the DPD status and the socio-demographic patterns of the users. Our results show that the DPD survey meets the internal consistency reliability with rigorous statistical measures, e.g., Cronbach's $\alpha=0.92$. Furthermore, the DPD index is shown to capture the underlying communality of all DPD variables. Finally, the DPD proportional odds model indicates a strong statistical correlation between the DPD status and the age groups of the ICT users. For example, we find that young users (15-32 years) are generally more concerned about their digital privacy than senior ones (33 years and over).

ПРОГРАМНІ МЕТОДИ МОНІТОРИНГУ МЕРЕЖЕВОЇ БЕЗПЕКИ

The software methods for monitoring network security (NSM - Network Security Monitoring) are discussed. With the growth and rapid development of mobile communications, rich data and artificial intelligence technologies, we are entering the era of the mobile Internet. With the continuous intellectualization of network security and infrastructure, information technology is widely used in the field of industrial control, making network security more and more open, bringing a new network security control system to the traditional relatively closed industrial control system. Currently, there is an increase in the number of information threats and factors leading to the unstable operation of data transmission networks. The prerequisites for this growth are the mass application, the complication of the hierarchy of computer networks and the increase in their structural complexity, the increase in the heterogeneity of software and hardware, the complication of the functionality of network services, which leads to the emergence of various vulnerabilities. Under such conditions, the development and improvement of methods for identifying information threats are of great importance. One of the components of ensuring information protection of networks is software systems designed to detect harmful or suspicious activity - network security monitoring methods (NSM). Network security monitoring techniques (NSM) are used to monitor network 133 communications for information security events. For maximum effect, a combination of capturing the entire packet in addition to logging network activity is recommended. This article provides definitions of network security monitoring methods, their classification, phases of the method cycle and their description. Some of the best known and widely used multi-module NSM solutions have been reviewed. The best known examples of such combinations are IDS/IPS, SEM/SIEM and UTM. Network security monitoring is important because it checks if the first lines of defense are working, gives us the opportunity to eliminate threats before they cause real damage if there is a vulnerability somewhere in your system, and allows us to understand where these vulnerabilities are and how to fix them before something will happen.

Challenges in security and privacy in wireless communications

This Research Review Paper highlights some of the main concepts of Wireless Communication including Network Security, Privacy, and Transmission Control Protocol/Internet Protocol (TCP/IP). In equal manner, it illustrates the organizational importance to establish more effective policies to counterattack and prevent cyber-attacks and reduce cyber threats. Therefore, avoiding the financial cost occasioned by hackers and the physical harm that could be cause to an individual by the sensitive data shared via technological devices to the internet without permission.

Analysis and Application of intelligent Power Control System Cyber Security Situation Awareness Based on Wavelet Neural Network

In recent years, international industrial control network security incidents have occurred frequently. As a core component of the industrial control field, intelligent power control systems are increasingly threatened by external network attacks. Based on the current research status of power industrial control network security, closely combining the development of active monitoring and defense technology in the public network field and the problems encountered by network security operators in actual work, this paper uses data mining methods to study the power control system network security situation awareness technology. Combing operational data collection and integrated processing, situation index screening and extraction, we use wavelet neural network analysis method to train the sampled data set, and finally calculate the true value of the network security status through deep intelligent learning. Finally, we conclude that the artificial intelligence algorithm based on wavelet neural network can be used for power control system network security situation awareness. In actual work, it can predict the situation value for a period of time in the future and assist network security personnel in judgment and decision-making.

Virtual Network Security Control Mechanism based on Multi-level Alerting and Linkage Defense

The intrusion detection system can effectively detect network attacks dynamically by analyzing network traffic or data packets. However, for complex multi-step attacks with high concealment and persistence, traditional intrusion detection systems generally have the problems of large redundancy of alert data and poor data readability, which seriously affects the security administrators to quickly identify attack behaviors and intentions. To address the above problems, this paper proposes a multi-level alert aggregation method based on software-defined security and linked correction defense based on the Markov chain model. This method designs the reporting message format between the data plane security component and the control plane security controller, and automatically extends the time window by using the temporal proximity relationship of alarms. Multi-level alert aggregation is performed based on the attack attribute matching method, resulting in the effective aggregation of similar alarms. Further, the control plane uses the Markov chain model to generate alert association graphs to obtain the transfer probability between attack types, which is sent down to the security components for linkage defense. The experiments show that this mechanism can effectively achieve multi-level alarm aggregation and alarm information association linkage.

Network Security Monitoring Method Based on Deep Learning

AbsrtactWith the rise and rapid development of mobile communication, big data and artificial intelligence technology, we are entering the era of mobile Internet. With the continuous intellectualization of network security and infrastructure, information technology has been widely used in the field of industrial control, making network security more and more open, which brings a new network security control system to the traditional relatively closed industrial control system. This paper mainly introduces the network security monitoring method based on deep learning. In this paper, the network security monitoring method based on deep learning is studied, and the network security monitoring planning is designed by using image analysis, and the feasibility of detection model and deep learning is analyzed reasonably. A network security monitoring method based on deep learning is designed. Through data collection, feature extraction and neural network model training of network security power consumption information, non-invasive network security monitoring is realized. This method can detect network security information attacks that cannot be found at the network level, and improve the security performance of network security. The experimental results show that the network security monitoring method for deep learning increases the network security efficiency by 24%. The limitations of the research on network security monitoring method for deep learning and the methods and paths to provide good network security monitoring planning for image analysis application are analyzed, discussed and summarized, so as to enrich the academic research results.

Analysis of Internet of Things Computer Network Security and Remote Control Technology

The Internet of Things system is a product of the combination of information technology in the rapid development of computer technology, and it is an important foundation for the development of the information age. But we cannot ignore computer network security issues in the application of the Internet of Things. In order to effectively control the security issues of the Internet of Things, we have to study and analyze the potential security risks in the current Internet of Things computer network. In addition, we should explore reliable network security control measures and computer remote control technology. Only in this way can the further development of the IoT system be promoted.

Data-Driven Cybersecurity Knowledge Graph Construction for Industrial Control System Security

Industrial control systems (ICS) involve many key industries, which once attacked will cause heavy losses. However, traditional passive defense methods of cybersecurity have difficulty effectively dealing with increasingly complex threats; a knowledge graph is a new idea to analyze and process data in cybersecurity analysis. We propose a novel overall framework of data-driven industrial control network security defense, which integrated fragmented multisource threat data with an industrial network layout by a cybersecurity knowledge graph. In order to better correlate data to construct a knowledge graph, we propose a distant supervised relation extraction model ResPCNN-ATT; it is based on a deep residual convolutional neural network and attention mechanism, reduces the influence of noisy data in distant supervision, and better extracts deep semantic features in sentences by using deep residuals. We empirically demonstrate the performance of the proposed method in the field of general cybersecurity by using dataset CSER; the model proposed in this paper achieves higher accuracy than other models. And then, the dataset ICSER was used to construct a cybersecurity knowledge graph (CSKG) on the basis of analyzing specific industrial control scenarios, visualizing the knowledge graph for further security analysis to the industrial control system.

Application of information communication network security management and control based on big data technology

SummaryWith the continuous development of communication technology and computer network technology and the continuous progress of social economy, information technology has penetrated into all aspects of people's lives. The basic task of communication network is to ensure the reliability and security of information transmission. Based on the above background, the purpose of this study is to study the application of information communication network security management and control based on big data technology. Based on the concept and management and control structure of information communication network, combined with the data collection and preprocessing in big data technology, this study analyzes the feasibility of big data technology applied in communication network management and control and expounds the specific application of big data technology from the four aspects of troubleshooting, security protection, business management, and resource allocation. At last, the test environment of information communication network security management and control platform based on big data technology is built in the laboratory to test the network management performance and routing transmission performance of the system. The experimental results show that the communication success rate of the information communication network security management and control platform based on big data technology remains to 91.78%. It can be seen from the test results that the information communication network security management and control platform based on big data technology can not only accurately collect sensor data but also have the function of real‐time monitoring network status and reconfiguration of nodes.

Securing IoT Devices Using Zero Trust and Blockchain

ABSTRACT The benefits ushered through the use of IoT come with its share of security concerns. IoT networks are heterogeneous and existing network security tools and controls are inadequate for them. The IoT devices and the data transmitted to and from these devices remain exposed to a multitude of threats. In this paper, we review the security concerns involved in the implementation of IoT and propose a framework for security of IoT devices based on Zero Trust and blockchain. A risk-based segmentation of IoT network increases the homogeneity of IoT device attributes and communication protocols. Zero Trust extends the perimeter of trust beyond the IT/OT network. Blockchain improves the device identification and access control capabilities of the IoT network. We support the conceptual framework with a case study on the implementation of IoT security using Zero Trust and blockchain. We provide a risk-scoring method and a five-point recommendation for management of IoT security. Our proposed IoT security framework will help implementors of IoT to overcome existing security concerns and will benefit academic researchers and practitioners alike.