Network Security Risk Research Articles

Standardized research on multifunctional beacon multi-sensor data acquisition and transmission

Abstract This paper discusses the research on the standardization of data acquisition and transmission for multifunctional beacon systems. The multifunctional beacon system integrates various types of sensing devices and can provide real-time, comprehensive and accurate marine environment and navigation information for ships and shore-based users. However, the system currently faces challenges such as non-uniform data formats and communication protocols, low hardware standardization and integration, and data and network security risks. In order to solve these problems, this paper puts forward the solution of “standard terminal for data acquisition and transmission”, which achieves the compatibility of equipment interface, unified data format, and reliable information security. It lays the foundation for the standardization of multi-purpose beacon system. Through modular design and standardized packaging, the performance, reliability and efficiency of the hardware of multi-purpose beacons are improved, the development and maintenance costs are reduced, and the flexibility of the system is enhanced. The “Standard Terminal for Data Acquisition and Transmission” is applied to the multi-purpose beacon system, and interfaces and integrates with the existing “Operation Management System for Navigation Guarantee” to realize a unified platform for multi-purpose beacons. This research helps to promote the formulation of multi-purpose beacon data format and communication protocol standards at the national or industrial level, provides important reference for the formulation of international standards, and is of great significance in promoting the standardized development and application promotion of multi-purpose beacon technology.

The Role of Cloud Computing in Shaping Network Security: Implications for Organizational Behavior

Cloud computing is currently provided consumer or business IT support via social media or by using the internet. Cloud computing on the other hand, is increasing the level of the network security risk due to the services are basically presented by a third party. This results in hard to control the privacy and data security. In addition, to maintain the service availability and support data collections. Cloud computing clouts several technologies such as virtualization, SOA, and Web 2.0, it is also claimed their security risk matters. In this paper, the most serious and important risks and threats of cloud computing are discussed. The main vulnerability is identifying through a review of the published works on the cloud computing environment with possible solutions to overcome these threats and risks.

Sterilization of image steganography using self-supervised convolutional neural network

Background With the development of steganography technology, lawbreakers can implement covert communication in social networks more easily, exacerbating network security risks. Sterilization of image steganography methods can eliminate secret messages to block the transmission of illegal covert communication. However, existing methods overly rely on cover-stego image pairs and are unable to sanitize unknown image, which reduces stego image blocking rate in social networks. Methods To address the above problems, this paper proposes an effective sterilization of image steganography method using self-supervised convolutional neural network (SS-Net), which does not require any prior knowledge of image steganography schemes. SS-Net includes a purification module and a refinement module. Firstly, the pixel-shuffle down-sampling in purification module is adopted to reduce the spatial correlation of pixels in the stgeo image, and improve the learning mode from supervised learning to self-supervised learning. Secondly, centrally masked convolutions and dilated convolution residual blocks are merged to eliminate secret messages and avoid image quality degradation. Finally, a refinement module is employed to improve image texture details and boundaries. Results A series of experiments show that SS-Net from BOSSbase test sets is able to balance the destruction of secret messages with image quality, achieving 100% blocking rate of stego image. Meanwhile, our method outperforms the state-of-the-art methods in secret messages elimination ability and image quality preserving ability.

Secured THz communication in photonic microcell networks based on spatial wave mixing of steered beams

Future 6G communication systems are envisioned to expand their carrier frequency to the THz region, where a broad unexplored region of spectrum is available. With this expansion, THz wireless communication has the potential to achieve ultra-high data transmission rates of up to 100Gbit/s. However, as large amounts of data are transmitted in an open wireless environment, there are significant concerns regarding communication security due to the susceptibility to eavesdropping, interception, and jamming. In this work, we proposed a secure approach for THz wireless communication based on spatial wave mixing and flexible beam steering. To achieve this, two frequency-modulated THz waves, which are generated by photonic THz sources and carry encrypted information with true randomness, are mixed at a THz envelope detector with an exclusive-OR logic operation. We analyzed the possible spatial location for the THz detector to ensure a secure microcell network deployment. Our results demonstrate that the size of the decryptable region is directly dependent on the directivity and width of the emitted THz beam. To address this, we have developed an array antenna with integrated uni-traveling-carrier photodiodes (UTC-PDs), which is capable of generating THz waves while also improving the flexibility of beam pointing, allowing for greater control over the location and size of the decodable region. By controlling fiber-optic delay lines, we successfully demonstrated that the directional gain of a 200GHz wave is increased by 8dB through a 1 × 3 UTC-PD-integrated planar bowtie antenna (PBA) array, together with continuous beam steering from -20° to 10°. Additionally, using a 1 × 4 UTC-PD-integrated PBA array to emulate two encryption transmitters and a Femi-level managed barrier diode to detect spatially mixed THz waves, we successfully achieved a feasibility experiment for real-time 200Mbit/s location-based decryption in the 200GHz band. These results indicate that the proposed scheme is feasible for secured THz communication, and would be a powerful candidate to mitigate security risks in 6G microcell networks.

A Review of Endogenous Security Research

The development of society has deepened the application of the Internet in production and daily life. At the same time, network security risks are becoming increasingly severe. For the security problems faced in cyberspace, most of the traditional defenses are currently focused on blocking the discovered vulnerabilities. However, these methods not only rely on prior knowledge of vulnerabilities but also fail to address the security issues brought about by the protection program itself. In view of this, endogenous security, which emphasizes the importance of not relying on a priori knowledge and not bringing in new security problems, has received increasing attention. This review provides a detailed introduction to endogenous security and its related issues, which is lacking in the field of network security. Firstly, this paper outlines the detrimental effects of vulnerabilities, identifies issues within moving target defense, and contrasts it with mimic defense. Additionally, the concepts, models, principles, and application scenarios of endogenous security are introduced. Finally, the challenges encountered by this technology are comprehensively summarized, and potential future development trends are further explored.

Influence of subsidy policies against insurances on controlling the propagation of epidemic security risks in networks

Subsidies are used to further control the propagation of epidemic security risks as they offer users incentives to practice security investment behaviors. However, previously proposed subsidy policies have typically been studied without insurance, and it is still challenging to build effective subsidy strategies when users can transfer some of the infection loss to the insurer by purchasing insurance, because these external incentives may alter users' decisions to purchase insurance. To this end, we designed three subsidy policies in a scenario where purchasing insurance is one of the available strategies to users: under SPIns and SPInsp, the subsidies are used as the insurance funds and the risk precaution aspect of the insurance funds, respectively, and under SPInsd, a fraction of the subsidies are used to support some free-riding users with high node importance in their purchase of insurance, and the remaining subsidies are used as the precaution aspect of the insurance funds. The subsidy policies are studied with a formulated security investment game model on scale-free networks from the perspectives of the public and insurer. The results show that SPInsd can always work effectively and outperforms others in limiting the extent of an epidemic outbreak with similar or lower social costs, and without a reduction in profit for the insurer. More importantly, an interesting phenomenon emerges in the SPInsp scenario: under specific insurance parameters, the increase in subsidy funding has a negative impact on preventing the risk spread, leading to larger final epidemic sizes. In addition, the effectiveness of SPInsd on scale-free networks with larger average degree or network size and random networks are also studied. We anticipate this work can provide useful insights for policy makers with respect to design and implementation of optimal subsidy policies related to the control of epidemic security risks under an insurance scenario.

Critical Infrastructures at Risk Addressing Information Security in Power Systems

According to the power system network security risks and problems, this paper presents the work of information security and the general principles of information security from a technical and safety management put forward the idea of addressing security issues and methods of analysis of local area network security management related to issues, and according to their own experience of a relatively effective way of thinking.

Construction of power network security risk assessment model based on LSA-SVM algorithm in the background of smart grid

Due to theintricate and interdependent nature of the smart grid, it has encountered an increasing number of security threats in recent years. Currently, conventional security measures such as firewalls, intrusion detection, and malicious detection technologies offer specific protection based on their unique perspectives. However, as the types and concealment of attacksincrease, these measures struggle to detect them promptly and respond accordingly. In order to meet the social demand for the accuracy and computation speed of the power network security risk evaluation model, the study develops a fusion power network security risk evaluation algorithm by fusing the flash search algorithm with the support vector machine. This algorithm is then used as the foundation for building an improved power network security risk evaluation model based on the fusion algorithm.The study's improved algorithm's accuracy is 96.2%, which is higher than the accuracy of the other comparative algorithms; its error rate is 3.8%, which is lower than the error rate of the other comparative algorithms; and its loss function curve convergence is quicker than that of the other algorithms.The risk evaluation model's accuracy is 97.8%, which is higher than the accuracy of other comparative models; the error rate is 1.9%, which is lower than the error rate of other comparative models; the computing time of the improved power network security risk evaluation model is 4.4 s, which is lower than the computing time of other comparative models; and its expert score is high. These findings are supported by empirical analysis of the improved power network security risk evaluation model proposed in the study. According to the study's findings, the fusion algorithm and the upgraded power network security risk evaluation model outperform other approaches in terms of accuracy and processing speed. This allows the study's maintenance staff to better meet the needs of the community by assisting them in identifying potential security hazards early on and taking the necessary preventative and remedial action to ensure the power system's continued safe operation.

Exploration on human resource management and prediction model of data-driven information security in Internet of Things

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study's algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

A comprehensive survey of performance, security and privacy issues in the network interface layer of the TCP/IP

The network interface layer of the TCP/IP protocol suite, primarily comprised of the Internet Protocol (IP), serves as the backbone of modern internet communication. With its efficient data delivery, The network interface layer, presents key challenges in terms of performance, security, and privacy. This comprehensive survey delves into these three crucial aspects, analyzing the inherent vulnerabilities, limitations of the interface layer, and provide solutions of the related problems. The performance analysis explores throughput, latency, and bandwidth constraints, along with solutions such as bandwidth allocation and optimization techniques. Vulnerabilities within Network Interface Layer, including denial-of-service attacks and MAC address spoofing, are discussed, along with a review of existing security mechanisms. Privacy flaws are examined, covering MAC address tracking, profiling risks, and anonymization techniques, while also addressing privacy considerations on the Internet of Things. The survey analyzes several case studies providing comparative analysis of the network interface layer protocols, with support of the real world scenarios including performance analysis in high density environment, and security and privacy risks in smart homes networks. The findings provide a comprehensive understanding of the complexities surrounding performance, security, and privacy issues future directions and potential solutions.

Predict and Optimize Financial Services Risk Using AI-driven Technology

With the rapid development of internet technology, many industries have embarked on a digital transformation. However, while the Internet has brought convenience to users, it has also become a breeding ground for criminals to commit fraud. On the one hand, a large number of users on the Internet more or less left data, criminals can use this information to practice accurate fraud users, improve the success rate of fraud; On the other hand, online financial transactions such as banking and e-commerce also provide more opportunities for criminals to commit fraud. Therefore, all kinds of fraud methods emerge in an endless flow, through the telephone, information, fishing and other means of fraud, not only to bring hundreds of millions of losses to society every year, but also to the security of people's lives have a huge threat. Monitoring and preventing online fraud is an important part of the cybersecurity industry. For known network fraud, based on the domain name of the phishing site, the account number and mobile phone number that send fraudulent information, simple and effective monitoring and defence can be carried out through the blacklist. However, it is difficult for traditional means to effectively defend against undocumented fraud. With the development of machine learning technology, it is the main research direction of fraud detection methods to discover the information sources and characteristics of information content through machine learning technology, and make real-time and continuous accurate judgments. This paper realises credit fraud detection by generating adversarial network technology, so as to prevent network security risks.

APT Attack Detection of a New Power System based on DPI-transformer

Introduction: In recent years, the frequent occurrence of network security attacks in the power field has brought huge risks to the production, transmission, and supply of power systems, and Advanced Persistent Threat (APT) is a covert advanced network security attack, which has become one of the network security risks that cannot be ignored in the construction of new power systems. Objective: This study aims to resist the increasing risk of APT attacks in the construction of new power systems, this paper proposes an attack detection model based on Deep Packet Inspection (DPI) and Transformer. Methods: Firstly, we extracted 606 traffic characteristics from the original traffic data through the extended CIC Flowmeter and used them all to train the Transformer network. Then, we used the DPI-Transformer model and traffic labels to perform feature analysis on the traffic data and finally obtained the APT-Score. If the APT-Score is greater than the threshold, the alarm module is triggered. Results: By analyzing the headers and payloads of the network traffic in the APT-2020 dataset, the experimental results show that the detection accuracy of APT attacks by the DPI-Transformer detection model is significantly higher than that of the current mainstream APT attack detection algorithms. Conclusion: Combined with the characteristics of the new power system and APT attacks, this paper proposes an attack detection model DPI-Transformer, which proves that the model has greatly improved the detection accuracy.

Network Security Governance Policy and Risk Management: Research on Challenges and Coping Strategies

Cybersecurity is a big issue for major multinational corporations in today's lightning-fast digital world. Risk management and Network Security Governance (NSG) are complex, and this paper discusses the challenges and strategies needed to protect digital assets in a more vulnerable cyber environment. Cyber threats are constantly changing, technological integration is complex, and regulatory compliance is severe, all of which make it more challenging to maintain robust network security. NSG requires strong security rules and standards, which this conversation must address. The ever-changing threat environment demands that these regulations be open, accurate, and flexible. Risk management identifying, assessing, and mitigating threats—is essential to regulatory compliance and organizational reputation, according to the article. Risk mitigation methods like proactive, investigative, and remedial approaches are examined, along with cybersecurity advancements like Artificial Intelligence (AI) and Machine Learning (ML). In solving network security issues, the text emphasizes continuous learning, collaboration, and information sharing. Network Security Governance and Risk Management (NSGRM) is complex and dynamic, and this study covers its challenges and strategies.

Construction of Cybersecurity and Risk Prediction Model for New Energy Power Plants under Machine Learning Algorithm

Abstract Addressing the complex equipment and network challenges in new energy power plant industrial control systems, this study introduces a Markov time-varying machine learning algorithm, leveraging classification-constrained Boltzmann machines for real-time network security risk prediction. By employing a hybrid training mode for innovative feature extraction and classification, the algorithm forecasts future security risk states through an up-to-date state transition probability matrix. Integrating with the Markov time-varying model enhances the efficiency over traditional Boltzmann machines, facilitating nuanced network state analyses. The proposed model demonstrates high effectiveness against various network attacks, with average precision, recall, and F1 scores of 0.96, 0.93, and 0.94, respectively, and maintains over 80% accuracy under noise levels up to 40 dB. This research provides a solid foundation for proactive security defense mechanisms in industrial control systems.

Financial Network Security Risk Intelligent Prediction Algorithm based on HMM-LDA Model

Financial Network Security Risk Intelligent Prediction Algorithm based on HMM-LDA Model

Key security measurement method of authentication based on mobile edge computing in urban rail transit communication network

In the rail transit industry, the communication network is also widely used in various professions. Rail transit stations have the characteristics of narrow space, high degree of airtightness, limited sight lines, and uneven distribution of personnel in various periods. Although the security degree of rail transit entrances and exits is relatively reliable compared with that of stations, the anti-risk ability is fragile, and the consequences are often disastrous. To solve the problems of low-security access rate, long response time and ineffective identification of attack components of urban rail transit communication platform users, a security measurement method based on mobile edge computing (MEC) for the authentication key of urban rail transit communication network is proposed. Firstly, the wireless communication service of the urban rail transit platform is analyzed, and the data key protocol is constructed to obtain the hyper-chaos key pair of wireless communication information. Then, the double chaotic mapping model is applied to determine the key form and input parameters, and the bit key stream is obtained, which is subjected to exclusive operation with the plaintext for key decryption. Next, the block matching structure is used to analyze the associated eigenvalues that affect the security risk of urban rail transit communication networks. Meanwhile, the MEC server is used to accelerate the extraction of its feature values. Finally, in the environment of MEC, the paper constructs a risk identification model, evaluates the security strength of the authentication key, controls the threshold value, analyzes the impact factors and weights of communication network security, and completes the traceability measurement of communication network security risk. Experimental results show that the security access times of the MEC platform are senior, the maximum attack component obtained is 23500, which is consistent with the actual value, and the global maximum response time is 0.30 ms.

Large Language Model in SD-WAN Intelligent Operations and Maintenance

The integration of LLM (Large Language Model) into the intelligent operations and maintenance of SD-WAN (Software-Defined Wide Area Networks) is comprehensively explored. As the scale of the network continues to expand and its complexity increases, traditional operation and maintenance methods can no longer meet efficient and accurate requirements. Therefore, LLM technology was introduced, and the intelligent operation and maintenance of the data center network were realized by leveraging its powerful natural language processing capabilities. The importance and shortcomings of SD-WAN and the application scenarios of LLM in network operation and maintenance are first introduced, followed by an elaboration on how LLM can be used for network configuration, network optimization, network maintenance, and network security risk management. Through specific analysis, the significant effect of LLM in enhancing the efficiency and quality of operation and maintenance has been demonstrated. Finally, the advantages and challenges of employing LLM for intelligent network operation and maintenance are summarized, and future research directions are anticipated.

Research on Network Security Situation Assessment Model in Decentralized Computing Environment

Abstract In this paper, decentralized computing is used as an entry point to review the latest research results on network security issues through two methods, namely task assignment and hierarchical analysis, and construct a network security posture assessment model based on decentralized computing. A control framework is constructed by utilizing the functional complementarity of continuous authentication and security threat assessment in order to facilitate real-time observation of the security situation of the network and timely elimination of malicious nodes. A quantitative network security threat posture assessment model is constructed through hierarchical analysis to observe the extent of the breach of confidentiality and integrity of network information based on the security threat posture index. The effectiveness of the network security posture assessment model and method proposed in this paper was verified by empirical analysis in a simulated environment. The results show that after the simulated attack lasts for 12 minutes, the network security risk index measured by the assessment model in the test cascade case becomes larger with the intensification of the network attack, and the risk index value is up to 5.5. In summary, the network security posture assessment model based on decentralized computing designed in this paper can quickly reflect the changes in the security status of the underlying network and provide administrators with the current security status of the network in a macroscopic way.

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

Effect Analysis of Digital Communication of Social Media from the Perspective of New Media

At a time when everyone is using social media, new media gives people a new form of communication, digital communication. In the present digital communication, it has completely different characteristics from traditional media and changes peoples lives imperceptibly. People are no longer focused on the content of newspapers or magazines but more focused on global and personal content. Media is no longer something only professionals can use. Everyone can become a celebrity on the Internet. This essay analyzes the characteristics of digital communication brought by new media and what impression and harm it brings to creators, users, and advertisers, respectively. According to the research findings, social media has the characteristics of diversity, real-time, globalization and personalization. It gives creators a wider range of creative space, makes it easier for advertisers to find their target audience, and allows users to improve their Internet experience. While providing internet convenience, there are also risk of network security.