Security Protection Research Articles

Information security and data protection at an industrial enterprise in modern conditions

It is impossible to imagine a modern enterprise without automation and digitalization of production processes. At the same time, new ways of doing business in the digital era are accompanied by such information threats as: data leaks, encryption viruses and attacks on critical information infrastructure. All data and assets need protection. This is not only a business requirement, but also the requirements of laws on CII and the processing of personal data. This is where information security comes to the aid of companies and enterprises.

Retraction Note: Research on campus network security protection system framework based on cloud data and intrusion detection algorithm

Retraction Note: Research on campus network security protection system framework based on cloud data and intrusion detection algorithm

LSTM Based New Probability Features Using Machine Learning to Improve Network Attack Detection

This project focuses on improving the detection of network attacks by using a machine learning technique known as Long Short-Term Memory (LSTM) networks. LSTM networks are a type of neural network that excels at analyzing sequences of data, making them well-suited for identifying patterns associated with network intrusions. To enhance the LSTM model's effectiveness, we introduce new probability features that help the model better distinguish between normal and malicious activities. Our approach includes collecting network data, preprocessing it to make it suitable for training, and then using this data to train the LSTM model. We evaluate the model's performance using a range of metrics to ensure its accuracy and reliability. The results indicate that our method significantly improves the detection rate of network attacks while also reducing the number of false alarms. This means that our LSTM-based model not only catches more real threats but also makes fewer mistakes in identifying normal activities as attacks. Overall, this project showcases the potential of advanced machine learning techniques, like LSTM networks, to enhance cyber security measures and protect against network threats more effectively.

Software installation threat detection based on attention mechanism and improved convolutional neural network in IOT platform

With of the Internet of Things (IoT) developing and the network technique progressing, malware attacks continue to occur, seriously endangering the information and property security of Internet of Things device users. To ensure the security of the Internet of Things platform and improve the efficiency of malware and vulnerability detection, a software installation threat detection model based on attention mechanism and improved convolutional neural network is constructed. Firstly, the enhanced dynamic symbolic execution module and forward program slicing algorithm are used to extract dynamic features, and then the improved convolutional neural network is utilized to classify malware. In the existing software of IoT devices, the inlining correlation function is studied using the inlining strategy, and the weight between the target pixel and the global pixel is calculated using the attention mechanism, through which the logic and correlation between the triples are correlated. Then, deep residual network is used to detect software vulnerabilities. This enables threat detection before and after software installation. In comparison with the current popular vulnerability detection model experiments, the accuracy, recall rate, accuracy rate and running time of the constructed model in the process of vulnerability detection are 0.975, 0.970, 0.968 and 0.02 s, respectively. Compared with other models, the research design model has better performance. This shows that this built model can effectively detect software installation threats, and has high detection accuracy and operation efficiency, which can provide strong support for the Internet of Things platform’s security protection.

Research on Power Cyber-Physical Cross-Domain Attack Paths Based on Graph Knowledge

Against the background of the construction of new power systems, power generation, transmission, distribution, and dispatching services are open to the outside world for interaction, and the accessibility of attack paths has been significantly enhanced. We are facing cyber-physical cross-domain attacks with the characteristics of strong targeting, high concealment, and cross-space threats. This paper proposes a quantitative analysis method for the influence of power cyber-physical cross-domain attack paths based on graph knowledge. First, a layered attack graph was constructed based on the cross-space and strong coupling characteristics of the power cyber-physical system business and the vertical architecture of network security protection focusing on border protection. The attack graph included cyber-physical cross-domain attacks, control master stations, measurement and control equipment failures, transient stable node disturbances, and other vertices, and achieved a comprehensive depiction of the attack path. Second, the out-degree, in-degree, vertex betweenness, etc., of each vertex in the attack graph were comprehensively considered to calculate the vertex vulnerability, and by defining the cyber-physical coupling degree and edge weights, the risk of each attack path was analyzed in detail. Finally, the IEEE RTS79 and RTS96 node systems were selected, and the impact of risk conduction on the cascading failures of the physical space system under typical attack paths was analyzed using examples, verifying the effectiveness of the proposed method.

The facilitators and barriers to implementing virtual visits in intensive care units: A mixed-methods systematic review.

Visitation has a positive effect on patients and families, yet, it can disrupt intensive care unit (ICU) care and increase the risk of patient infections, which previously favoured face-to-face visits. The coronavirus disease 2019 (COVID-19) pandemic has raised the importance of virtual visits and led to their widespread adoption globally, there are still many implementation barriers that need to be improved. Therefore, this review aimed to explore the use of ICU virtual visit technology during the COVID-19 pandemic and the barriers and facilitators of virtual visits to improve virtual visits in ICUs. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, six databases (CINAHL, China National Knowledge Infrastructure [CNKI], PubMed, Cochrane, VIP and Wang Fang databases) were searched for empirical studies published between 1 January 2020 and 22 October 2023. Studies that investigated and reported barriers to and facilitators of implementing virtual visits in ICUs during the COVID-19 pandemic were included. Evidence from the included studies was identified and thematically analysed using Thomas and Harden's three-step approach. Study quality was appraised with the Mixed-Methods Appraisal Tool. A total of 6770 references were screened, of which 35 studies met the inclusion criteria after a full-text review. Eight main barriers to virtual visits use were identified: technical difficulties; insufficient resources; lack of physical presence and nonverbal information; low technical literacy; differences in families' perceptions of visual cues; privacy and ethics issues; inequitable access and use of virtual visit technology; and lack of advance preparation. Four facilitating factors of virtual visit use were identified: providing multidimensional professional support; strengthening coordination services; understanding the preferences of patients and their families; and enhancing privacy and security protection. In the quality appraisal of 35 studies, 12 studies were rated as low, five as medium and 18 as high methodological quality. This review identified key facilitating factors and barriers to ICU virtual visits, which can foster the development of infrastructure, virtual visiting workflows, guidelines, policies and visiting systems to improve ICU virtual visiting services. Further studies are necessary to identify potential solutions to the identified barriers.

A Blockchain Copyright Protection Scheme Based on CP-ABE Scheme with Policy Update.

Although the copyright protection schemes supported by blockchain have significantly changed traditional copyright data management, there are still some data security challenges that cannot be ignored, especially the secure access and controllable management of copyright data. Quantum computing attacks also pose a threat to its security. Targeting these issues, we design and propose a blockchain copyright protection scheme based on attribute-based encryption (ABE). In this scheme, the security advantages of blockchain technology are utilized to ensure the authenticity and integrity of copyright data. Based on lattice cryptography and the decision ring learning with errors (R-LWE) problem, a new ABE algorithm that supports searchable ciphertext and policy updates is designed. Then, we introduce it into the blockchain copyright protection scheme, which enables secure access to copyright data and fine-grained control. In addition, the lattice cryptography can strengthen this scheme against quantum attacks. Through security analysis, our scheme can prove to be secure against adaptive chosen keyword attacks, selective chosen plaintext attacks, and adaptive chosen policy attacks in the random oracle model. More importantly, the comparison analysis and experimental results show that our proposed approach has lower computation costs and storage costs. Therefore, our scheme has better security and performance in copyright protection.

A zero-dimensional perovskite with high-efficiency luminescence and transient response for advanced information encryption

Due to the increasingly complex network attacks and advancements in computational technology, finding new and efficient encryption techniques is an urging task. As a promising material, the application of perovskite luminescent materials in information encryption is limited to pre-planned specific scenarios or applications, lacking the ability to respond to temporary demands. In this work, we have designed a novel cadmium-based zero-dimensional perovskite, TEA2CdCl4, which presents blue luminescence centered at 413 nm originating from organic cations. Upon Sb3+ doping, it not only exhibits intense red emission at 623 nm with a high photoluminescence quantum efficiency up to 100 %, but also brings outstanding resistance to thermal quenching. Especially, an ultrafast response speed could be achieved during decryption process which has been verified by first-principles calculations. Based on the advantages, a large-area optical anti-counterfeiting paper for temporary information encryption transmission is designed, which not only verifies the potential for secure information transmission and protection, but also introduces a new concept for the application of organic–inorganic hybrid perovskites in the field of information encryption.

A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN ENHANCING CYBERSECURITY THREAT DETECTION AND RESPONSE MECHANISMS

This literature review explores the transformative impact of artificial intelligence (AI) on enhancing cybersecurity measures across various domains. The study systematically examines the integration of AI in Intrusion Detection Systems (IDS), malware detection, phishing detection, threat intelligence, network security, and endpoint protection. Key findings reveal that AI-driven techniques significantly outperform traditional methods, particularly in real-time threat detection, accuracy, and adaptive response capabilities. Network-based IDS benefit from supervised and unsupervised learning algorithms, improving the identification of malicious network traffic and novel attack patterns. In malware detection, AI-enhanced static and dynamic analysis methods surpass signature-based approaches by detecting previously unknown malware and complex behaviors. Phishing detection has seen substantial improvements with AI applications in email filtering and URL analysis, reducing phishing incidents despite challenges like false positives. AI's role in threat intelligence is critical, automating data analysis to uncover hidden threats and employing predictive analytics to anticipate and mitigate cyber attacks. AI techniques in network security and endpoint protection enhance real-time monitoring and authentication processes, providing robust defenses against cyber intrusions. Despite these advancements, challenges such as handling high data volumes and the need for continuous learning to adapt to emerging threats remain. This review underscores the significant advancements, practical implementations, and ongoing challenges of leveraging AI in cybersecurity, highlighting its potential to fortify digital defenses and address the complexities of contemporary cyber threats.

Naval forces operational environment. Flexibility and strategic adaptability

In the dynamic scene of naval operations, constant adaptation and innovation are imperative to meeting the challenges and opportunities of technological advances, geopolitical changes and environmental considerations. This article aims to explore the multidimensional landscape of the naval operational environment, offering original insights and solutions to enhance naval capabilities and cooperation on a regional and global scale. From the forefront of technological innovation, onboard unmanned systems integration and artificial intelligence-based decision-making, to strategic adaptability in response to geopolitical dynamics, the study explores the complex interplay between naval strategy and emerging trends, with environmental considerations taking centre stage. The article argues for a collaborative global naval framework, highlighting the importance of international cooperation in addressing common maritime challenges. This framework envisages the creation of alliances, initiatives and partnerships to foster collective security, promote environmental protection and ensure the peaceful use of the world’s oceans. The main objective is to highlight the intersections between technological advances, strategic flexibility, environmental factors and international cooperation in shaping the future of naval operations. Overall, the study aims to engage a diverse audience comprising policymakers, maritime military leaders, researchers, academics and stakeholders involved in maritime security, defence and environmental protection.

Flexible Physical Unclonable Functions Based on Non-deterministically Distributed Dye-Doped Fibers and Droplets.

The development of new anticounterfeiting solutions is a constant challenge and involves several research fields. Much interest is currently devoted to systems that are impossible to clone, based on the physical unclonable function (PUF) paradigm. In this work, a new strategy based on electrospinning and electrospraying of dye-doped polymeric materials is presented for the manufacturing of flexible free-standing films that embed simultaneously different PUF keys. The proposed films can be used to fabricate novel anticounterfeiting labels having three encryption levels: (i) a map of fluorescent polymer droplets, with random positions on a dense yarn of polymer nanofibers, (ii) a characteristic fluorescence spectrum for each label, and (iii) the unique speckle patterns that every label produces when illuminated with coherent laser light shaped in different wavefronts. The intrinsic uniqueness introduced by the manufacturing process encodes enough complexity into the optical anticounterfeiting tag to generate thousands of cryptographic keys. The simple and cheap fabrication process as well as multilevel authentication makes such colored polymeric unclonable tags a practical solution in the secure protection of goods in our daily life.

Research on Information Security Management and Protection Measures of Financial Enterprises in the Era of Big Data---Take the USA of Industrial and Commercial Bank of China

Research on Information Security Management and Protection Measures of Financial Enterprises in the Era of Big Data---Take the USA of Industrial and Commercial Bank of China

Analysis of the Impact of Blockchain Technology Adoption on Operational Efficiency and Consumer Trust in Digital Business

This research intends to assess the influence of blockchain technology adoption on operational efficiency and consumer trust in digital enterprises. Blockchain technology, with its decentralized, transparent and safe properties, is predicted to have a favorable impact on several elements of business operations and boost consumer trust. The research method employed is a quantitative approach by collecting data through surveys and interviews with organizations that have incorporated blockchain technology. Data analysis was carried out using descriptive and inferential statistical techniques to test the stated hypotheses. The research results demonstrate that the deployment of blockchain technology greatly boosts operational efficiency, particularly in terms of reduced transaction costs, increased processing speed, and reduced operational errors. Apart from that, customer confidence has also improved dramatically, notably in areas of openness, security and data protection. These findings demonstrate that blockchain technology not only contributes to enhancing the operational performance of enterprises, but also boosting the trust relationship between firms and consumers. The conclusion of this study is that the implementation of blockchain technology has a considerable positive influence on operational efficiency and consumer trust in digital enterprises. Therefore, organizations working in the digital ecosystem are recommended to explore integrating blockchain technology as a strategy to boost competitiveness and consumer trust.

BREVE ANÁLISE ENTRE DIREITO BRASILEIRO E UNIÃO EUROPEIA ACERCA DA APLICAÇÃO DA INTELIGÊNCIA ARTIFICIAL

This scientific article aims to conduct a comprehensive analysis of the advances and challenges of artificial intelligence in the legal system, considering its ethical, social, and legal implications. Furthermore, the objective is to analyze the proposals of Brazilian legislations and establish a parallel with European law, with the purpose of identifying divergences and similarities. The development of these systems is based on the use of data from human experience, which highlights the inherent risks and has driven several countries to undertake legislative efforts for regulation. In this context, the European Union stands out, whose main objective is to create a trust-based ecosystem for development of AI. In order to understand the current approach to this topic and conduct a comparative study between the legal systems in question, practical examples of artificial intelligence implementation were analyzed. Additionally, a bibliographic survey of documents, legislative proposals, and studies published by the European Union, as well as relevant Brazilian legislation, was carried out. The results obtained revealed that the current Brazilian legislation constitutes a starting point in solving problems associated with the topic. However, as these technologies develop, there is a need for secure protection to prevent and repair damage. The presence of critical scientists, specialized in the area, is also important, as they directly influenced the legislative creations and the correct framing of AIs according to their risk.

Rib'ah: The Concept of Local Tradition Guarantee in Early Islam

<em>This article aims to express a word in the Sahifah Yathrib (Medinah Charter), namely rib'ah. This word contains quite a crucial meaning, however, it is rarely used daily by academics. Meanwhile in Indonesia, religious practices of peace and moderation still experience quite dangerous obstacles in society. All destruction of religious facilities, traditions, and even opposing cultures adhered to by the majority. This research is a literature study using the ma'na cum maghza framework initiated by SahironSyamsudin. With this framework, this research produces three main conclusions, namely: first, rib'ah has the basic meaning of dividing the spoils of war. However, in practice, the Prophet Muhammad guaranteed not only commerce but also religion. Second, rib'ah had its initial purpose as an effort by the Prophet Muhammad to unite the Muhajirin and Ansar, as well as for the development of a stable country. Third, the guarantee of freedom of religion and belief contained in this word can be used as a basis for hifz al-din, which means protection of security and comfort for a person to freely choose their religion and belief.</em>

Research and Application of a Generalized Power Communication Framework with Joint Security Protection Mechanism

Background: The rapid development of Industry 4.0 has opened up new power systems, which face a more complex network topology and a broader range of security threats. This has led to an urgent demand for a coordinated, lightweight, and secure communication management method. Methods: This paper presents a general power communication framework with a joint security protection mechanism that is suitable for coordinated management and transmission safety. The framework manages the protocol’s thread management of the device by converting it to the framework’s process management. It formulates specifications for protocols and equipment that access the framework to achieve support for multiple protocols and equipment. This reduces the difficulty of system operation and maintenance, as well as system complexity, and improves management efficiency. Additionally, the joint security protective mechanism for plugin authentication and protocol encryption is introduced to enable secure data transmission and guarantee the framework's security management. Finally, two experiments have been conducted to discuss and confirm the effectiveness and safety of the framework. Results: The results of the two experiments and the following analysis show that our framework is superior to the chosen well-known industrial commercial communication platforms, and the time delay cost by our mechanism doesn’t significantly affect the real-time data transmission in the power industry, proving its feasibility in both IoT and power applications. Conclusion: Our framework demonstrates exceptional performance in terms of time lag and packet loss during the experiment, surpassing other IoT communication frameworks. Furthermore, it maintains its high standards even under rigorous security analysis.

Application of Data Security Protection in Smart Grid Based on Computer Electronic Technology

Abstract With the continuous improvement of the intelligence level of the State Grid, data security protection in power system automation has become an important challenge for the development of power enterprises. In order to solve this problem, this article is based on the research of power system structure, data types, and the current development status of security protection, combined with computer electronics technology, to conduct in-depth research on data security protection in power system automation, and establish a security protection model based on its theoretical foundation. In order to verify the effectiveness of the model, this paper tested it from three dimensions: data security protection degree, risk processing efficiency, and response time, and compared it with traditional security protection models. The test results show that under different test cases, the maximum protection degree of the data security protection model proposed in this article reaches 95.16%, and the average protection degree is about 91.88%. From the specific test results, it can be seen that the application of data security protection in power system automation based on computer electronic technology has strong operability.

Research on Intelligent Cybersecurity Protection System in Cloud Computing Environment

In the digital era, cloud computing has transformed data processing, storage, and analysis with its scalability and flexibility. However, it also introduces new security challenges, such as data breaches and unauthorized access, which are exacerbated by Advanced Persistent Threats (APT) and zero-day vulnerabilities. This study aims to construct an intelligent network security protection system that adapts to the dynamic cloud computing environment, leveraging artificial intelligence and machine learning to enhance security efficiency and accuracy. The research objectives include developing a system that maintains privacy protection and user experience while ensuring secure access to cloud services. The paper presents a multi-layered architecture for the intelligent cybersecurity protection system, incorporating data collection, threat detection, access control, and adaptive security policies. Experimental methods and results validate the system’s effectiveness, demonstrating improved detection rates and reduced false positives compared to existing solutions. The study concludes with a discussion on the model’s advantages, limitations, and future application prospects in the field of cloud computing security.

A Study on the Forecasting of Real Estate Market Using Algorithms

Technology using algorithms has an impact on various aspects of the real estate market, such as real estate market price prediction, investment recommendation, and rent setting. First, it was confirmed that by improving the accuracy of real estate price prediction, real estate market price prediction using AI algorithm can derive more accurate results through machine learning and deep learning technologies based on past data. In addition, it is the development of an investment recommendation system. The investment recommendation system using AI can promote efficient fund management by discovering promising investment opportunities in the real estate market and proposing them to investors. The next is the optimization of lease on a deposit basis, monthly rent, and rent setting. The rent setting model using AI technology can set the rent appropriately through negotiations between real estate owners and tenants in consideration of market trends, demand, and predictions by region. Finally, it is the automation and efficiency of the real estate transaction process system. Smart contracts and automation systems using AI can save money and time between transaction parties by simplifying and streamlining the real estate transaction process. However, problems with data security and privacy protection are emerging as new challenges. New security and privacy problems arise in dealing with personal information, transactions, and data, and appropriate responses to them are urgent.

Revisiting vision-based violence detection in videos: A critical analysis

An ever-increasing installation of surveillance cameras at different places for ensuring public safety, security and asset protection has triggered the need for intelligent video surveillance to monitor the people and their behavior. Violence detection is a prominent application of intelligent surveillance as it plays a vital role in public safety, behavior monitoring, and law enforcement. It deals with identifying whether the violent event or behavior occurred in video sequence or not. Various researchers have developed different techniques and features for the detection of violence in recent years. The purpose of this paper is to provide an expository study of various state-of-the-art approaches for detecting violence in videos. It is evident from the ongoing effort in this field and with the advancement in computer vision technology, previous approaches get surpassed and new methods and features always keep on developing. Therefore, in order to complement ongoing research, it is also imperative to conduct a comprehensive analysis of different works from time to time. In this survey, each work has been critically analyzed, along with its pros and cons. The violence detection techniques have been divided under three categories: handcrafted features based, deep learning and hybrid violence detection approaches that have been extensively reviewed in various sub-categories. The major challenges faced by researchers and the steps involved in the process of violence detection are also discussed. Moreover, unlike typical practices of comparing the performance of models within same datasets, we examined a few models for cross-dataset assessment and revealed their limited generalization. This emphasizes the crucial need for robust model generalization to ensure efficacy across varied real-world scenarios. Finally, the paper carried out a discussion of various research gaps in the current approaches and the possible solutions to be taken to resolve them, laying a solid foundation for future work in this area.