Security Protection Research Articles

An Assessment of Multi-dimensional Factors Influencing Patronage of E-Commerce Channels in Beijing, China: Inputs for Loyalty Program

Cognizant of the growing Chinese e-commerce as one of the fundamental elements of the modern world because of technological advancements, this empirical study investigated e-businesses that utilize a variety of digital channels, such as social media, websites, and specialized applications, to market their products. Through a quantitative-descriptive research method, this study assessed the factors influencing e-commerce patronage, including product type, product quality, pricing competitiveness, user experience, and information security. The respondents were 300 customers of Tiktok, TaoBao, and JingDong in Beijing, China, chosen through quota sampling, who completed a survey questionnaire as the primary instrument for gathering data to answer research problems and test the hypothesis. The results revealed that user experience is highly influential in e-commerce patronage. The impact of these elements on patronage may vary depending on user preferences, platform design, and the overall shopping journey. The availability of a wide range of products on e-commerce platforms attracts customers looking for diverse options, influencing their patronage decisions. Competitive pricing strategies, including discounts, promotions, and price comparisons, significantly influence customers' choice of e-commerce channels. Trustworthiness and security of payment methods, data protection, and overall transaction security play a crucial role in attracting and retaining customers. The factors influencing e-commerce patronage are not significantly affected by demographic characteristics such as age, sex, educational attainment, and income.

Food Security and Plant Variety Protection: Seeding Hope for the African Continental Free Trade Area?

Food Security and Plant Variety Protection: Seeding Hope for the African Continental Free Trade Area?

GIG ECONOMY SHAKEUP : HOW IT’S RESHAPING JOBS, PAYCHECKS AND THE WORLD’S ECONOMY

The gig economy has arisen as a dynamic yet intricate factor in worldwide labor markets, providing workers with more flexibility but also posing substantial challenges concerning job security, wages, and legal protections. This research uses a bibliometric review of 980 academic articles and investigates significant elements influencing the gig economy, such as actual salaries, inflation rates, joblessness, data utilization, and gender interactions. Five primary groupings were detected, each illustrating the intricate connection among these variables. The results show that although the gig economy offers flexible work options, it also worsens problems like income inequality, exploitation, and gender disparities. Utilizing data-driven strategies and organizing labor movements are becoming more crucial in addressing these issues, providing gig workers with resources to enhance their work environments. This article discusses the economic and social impacts of the gig economy, and suggests policy suggestions to promote a fairer and more comprehensive job market for all employees.

Adaptive Scheduling Method of Heterogeneous Resources on Edge Side of Power System Collaboration Based on Cloud–Edge Security Dynamic Collaboration

In recent years, the large-scale integration of new power distribution technologies such as distributed power generation, electric vehicles, and flexible load control has led to a sharp increase in the operating pressure of the power cloud master station. To this end, an adaptive resource allocation method for edge-side general computing resources, which is used for cloud–edge collaborative security protection, is proposed. Firstly, considering the computing resources available to multiple edge substations, a Cloud–edge Collaborative Relay Business Security Protection Model (C2RBSPM) is constructed. Then, with the goal of minimizing the operating pressure of the maximum cloud master, the corresponding linear programming problem is established, and finally the Karush–Kuhn–Tucker (KKT) is used to solve it quickly. The simulation results show that the proposed method can reduce the expected operating pressure of the cloud master station by up to 35.19%. Therefore, reasonable mining of available computing resources on the edge side and relay security protection can effectively reduce the operating pressure of the cloud master station, and improve the operation efficiency of the system. This approach is of great significance for the flexible, intelligent, and digital transformation of the power distribution system in the future.

Optimizing Image Watermarking with Dual-Tree Complex Wavelet Transform and Particle Swarm Intelligence for Secure and High-Quality Protection

Watermarking is a technique used to address issues related to the widespread use of the internet, such as copyright protection, tamper localization, and authentication. However, most watermarking approaches negatively affect the quality of the original image. In this research, we propose an optimized image watermarking approach that utilizes the dual-tree complex wavelet transform and particle swarm optimization algorithm. Our approach focuses on maintaining the highest possible quality of the watermarked image by minimizing any noticeable changes. During the embedding phase, we break down the original image using a technique called dual-tree complex wavelet transform (DTCWT) and then use particle swarm optimization (PSO) to choose specific coefficients. We embed the bits of a binary logo into the least significant bits of these selected coefficients, creating the watermarked image. To extract the watermark, we reverse the embedding process by first decomposing both versions of the input image using DTCWT and extracting the same coefficients to retrieve those corresponding bits (watermark). In our experiments, we used a common dataset from watermarking research to demonstrate the functionality against various watermarked copies and peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics. The PSNR is a measure of how well the watermarked image maintains its original quality, and the NCC reflects how accurately the watermark can be extracted. Our method gives mean PSNR and NCC of 80.50% and 92.51%, respectively.

Incremental Machine Learning-integrated Blockchain for Real-time Security Protection in Cyber-enabled Manufacturing Systems

Abstract Cyber-enabled manufacturing systems become increasingly data-rich, generating vast amounts of real-time sensor data for quality control and process optimization. However, it also exposes these systems to significant cyber-physical security threats. For instance, attackers may delete, change, or replace data, leading to defective products, damaged equipment, or operational safety hazards. False data injection attacks can compromise machine learning (ML) models, resulting in erroneous outcomes. To mitigate these risks, it is crucial to employ robust data processing techniques that can adapt to varying process conditions and detect anomalies in real-time. In this context, incremental machine learning (IML) can be valuable, allowing models to be updated incrementally with newly collected data without retraining from scratch. Moreover, although recent studies have demonstrated the potential of blockchain in enhancing data security in manufacturing systems, most existing frameworks are primarily based on cryptography, which does not sufficiently address data quality issues. Thus, this study proposes a gatekeeper mechanism to integrate IML with blockchain and discusses how this integration could potentially increase data integrity of cyber-enabled manufacturing systems. The proposed IML-integrated blockchain can potentially address data security concerns from both intentional (e.g., malicious tampering) and unintentional alterations (e.g., process anomalies). The case study results show that the proposed gatekeeper integration algorithm can successfully filter out over 80% of malicious data entries while maintaining comparable classification performance to standard IML models. Furthermore, the integration of blockchain enables effective detection of tampering attempts, ensuring the trustworthiness of the stored information.

Data Security and Privacy Protection in Artificial Intelligence Models: Challenges and Defense Mechanisms

Artificial intelligence (AI) and deep learning algorithms are advancing rapidly, with these emerging technologies being widely applied in areas such as audio-visual recognition and natural language processing. However, in recent years, researchers have identified several security risks in current mainstream AI models, which could hinder the further development of AI technologies. As a result, the issues of data security and privacy protection in AI models have become a focus of research. The data and privacy leakage problems are primarily studied from two perspectives: data leakage based on model outputs and data leakage based on model updates. In the context of model output-based data leakage, the study discusses the principles and research status of model theft attacks, model inversion attacks, and membership inference attacks. In the context of model update-based data leakage, the research focuses on how attackers can steal private data during the distributed training process. Regarding data and privacy protection, three common defense methods are primarily studied: model structure defenses, information obfuscation defenses, and query control defenses. This paper reviews the cutting-edge research achievements in the field of data security and privacy protection in AI deep learning models, focusing on the theoretical foundations, key findings, and related applications of data theft and defense technologies in AI deep learning models. Keywords: Artificial Intelligence, Data Security, Privacy Leakage, Privacy Protection

Security Protection of Industrial Cyber Security with IIoT Through Zone Function Modeling Algorithm

Security Protection of Industrial Cyber Security with IIoT Through Zone Function Modeling Algorithm

A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security

Adversarial attacks were commonly considered in computer vision (CV), but their effect on network security apps rests in the field of open investigation. As IoT, AI, and 5G endure to unite and understand the potential of Industry 4.0, security events and incidents on IoT systems have been enlarged. While IoT networks efficiently deliver intellectual services, the vast amount of data processed and collected in IoT networks also creates severe security concerns. Numerous research works were keen to project intelligent network intrusion detection systems (NIDS) to avert the exploitation of IoT data through smart applications. Deep learning (DL) models are applied to perceive and alleviate numerous security attacks against IoT networks. DL has a considerable reputation in NIDS, owing to its robust ability to identify delicate differences between malicious and normal network activities. While a diversity of models are aimed at influencing DL techniques for security protection, whether these methods are exposed to adversarial examples is unidentified. This study introduces a Two-Tier Optimization Strategy for Robust Adversarial Attack Mitigation in (TTOS-RAAM) model for IoT network security. The major aim of the TTOS-RAAM technique is to recognize the presence of adversarial attack behaviour in the IoT. Primarily, the TTOS-RAAM technique utilizes a min-max scaler to scale the input data into a uniform format. Besides, a hybrid of the coati–grey wolf optimization (CGWO) approach is utilized for optimum feature selection. Moreover, the TTOS-RAAM technique employs the conditional variational autoencoder (CVAE) technique to detect adversarial attacks. Finally, the parameter adjustment of the CVAE model is performed by utilizing an improved chaos African vulture optimization (ICAVO) model. A wide range of experimentation analyses is performed and the outcomes are observed under numerous aspects using the RT-IoT2022 dataset. The performance validation of the TTOS-RAAM technique portrayed a superior accuracy value of 99.91% over existing approaches.

User Behavior Analysis and Security Strategy Optimization of College English Learning Platform Using Dynamic Graph Convolutional Network

This paper aimed to address the shortcomings of current college English learning platforms in user behavior analysis and security protection, especially the lack of accurate abnormal behavior detection and dynamic identity authentication measures. By adopting DGCN (Dynamic Graph Convolutional Network) and XGBoost (eXtreme Gradient Boosting) classification models for abnormal behavior detection, a dynamic identity authentication optimization strategy based on behavior risk scoring is proposed to improve the security of the platform. The user embedding features output from DGCN are input into XGBoost for user behavior classification. Based on the predicted probability of the XGBoost classification model, a risk score is generated for each user, and different identity authentication strategies are defined according to the risk score range. Experimental results show that by modeling multi-layer dynamic graph structures, the accuracy, precision, recall and F1 score of the XGBoost model reached 97.6%, 97.5%, 95.3%, and 96.4% respectively. In addition, when the step size of the DGCN model increases from 1 to 10, its accuracy only slightly decreases from 97.6% to 97.5%, and the user behavior classification performance is stable. The incidence of account theft with dynamic identity authentication is maintained between 3% and 8%, and the average authentication time is increased by 2.1 seconds compared with the traditional password authentication method. The proposed DGCN can optimize the security strategy of the college English learning platform based on the user behavior risk score, and it can select targeted identity authentication strategies according to the user's risk score.

TIBW: Task-Independent Backdoor Watermarking with Fine-Tuning Resilience for Pre-Trained Language Models

Pre-trained language models such as BERT, GPT-3, and T5 have made significant advancements in natural language processing (NLP). However, their widespread adoption raises concerns about intellectual property (IP) protection, as unauthorized use can undermine innovation. Watermarking has emerged as a promising solution for model ownership verification, but its application to NLP models presents unique challenges, particularly in ensuring robustness against fine-tuning and preventing interference with downstream tasks. This paper presents a novel watermarking scheme, TIBW (Task-Independent Backdoor Watermarking), that embeds robust, task-independent backdoor watermarks into pre-trained language models. By implementing a Trigger–Target Word Pair Search Algorithm that selects trigger–target word pairs with maximal semantic dissimilarity, our approach ensures that the watermark remains effective even after extensive fine-tuning. Additionally, we introduce Parameter Relationship Embedding (PRE) to subtly modify the model’s embedding layer, reinforcing the association between trigger and target words without degrading the model performance. We also design a comprehensive watermark verification process that evaluates task behavior consistency, quantified by the Watermark Embedding Success Rate (WESR). Our experiments across five benchmark NLP tasks demonstrate that the proposed watermarking method maintains a near-baseline performance on clean inputs while achieving a high WESR, outperforming existing baselines in both robustness and stealthiness. Furthermore, the watermark persists reliably even after additional fine-tuning, highlighting its resilience against potential watermark removal attempts. This work provides a secure and reliable IP protection mechanism for NLP models, ensuring watermark integrity across diverse applications.

A Proposal for a Zero-Trust-Based Multi-Level Security Model and Its Security Controls

The rapid advancement of technology and increasing data utilisation have underscored the need for new models to manage and secure big data effectively. However, the constraints of isolated network environments and the limitations of existing security frameworks hinder the adoption of cutting-edge technologies such as AI and cloud computing, as well as the safe utilisation of data. To address these challenges, this study proposes an enhanced security model that integrates the concepts of Multi-Level Security (MLS) and Zero Trust (ZT). The proposed model classifies data into the following three sensitivity levels: “Classified”, “Sensitive”, and “Open”. It applies tailored security requirements and dynamic controls to each level, enhancing both data security and usability. Furthermore, the model overcomes the static access control limitations of MLS by incorporating ZT’s automated dynamic access capabilities, significantly improving responsiveness to anomalous behaviours. This study contributes to the design and evaluation of a new security model that ensures secure data protection and utilisation, even in isolated network environments such as those of military and governmental organisations. It also provides a foundation for the future development of advanced security frameworks.

A Novel 5G Fronthaul Architecture Based on Quantum Security Protection

In the digital age, 5G technology has significantly enhanced global communication capabilities, providing strong support for various industries. However, 5G falls short of achieving comprehensive intelligence within the Internet of Things (IoT), propelling the development of 6G technology. 6G is expected to further enhance network performance by integrating advanced technologies such as AI and quantum communication, while expanding application scenarios to include communication in extreme environments for comprehensive global connectivity. This paper proposes an innovative fronthaul architecture that applies Quantum Key Distribution (QKD) technology to the 5G fronthaul, leveraging its unconditionally secure key generation and distribution mechanism. In this design, costlier Alice devices are placed on the AAU side, while less expensive Bob devices are positioned on the DU side, optimizing deployment to reduce engineering costs and lower deployment barriers. The feasibility of this architecture is demonstrated by calculating the secure key rate. Comparative analysis with existing research is performed to clarify future research directions. These innovations not only enhance 5G network security but also offer new solutions for the security requirements of 6G networks, such as data security, network resilience, and algorithm transparency. Our research offers strategic value for future network security, laying the groundwork for reliable networks.

A Comparative Study of Bitcoin Business Development and Customer Security Protection of Major Banks in the United States-Examining Wells Fargo, JPMorgan Chase, and Citibank as Case Studies

This essay provides an extensive comparative analysis of Bitcoin-related services among three major US banks—Wells Fargo, JPMorgan Chase, and Citibank—focusing on their business development, application conditions, and customer security protection mechanisms. By understanding the different approaches these banks take in handling Bitcoin services, the analysis sheds light on emerging trends in the banking sector's approach to cryptocurrency, customer risk assessment, and the growing importance of digital asset security. The study aims to present a comprehensive overview, highlighting the strengths and weaknesses of each bank in terms of their engagement with Bitcoin while also providing strategic recommendations for enhancing the overall Bitcoin service landscape in traditional banking.

Профессионально важные качества сотрудников уголовно-исполнительной системы в условиях цифровой трансформации

In the new socio-economic conditions, the requirements for professionally important qualities of specialists and the formation of their competencies are not just changing, but are being transformed and rebuilt, and this is primarily due to the restructuring of social processes and the introduction of digital technologies in various fields of professional activity. Professionally important qualities are understood as the individual properties of the subject of labor, a certain set of psychological characteristics that are necessary and generally sufficient for the implementation of effective and reliable professional activities, thus ensuring the success of its implementation. Understanding the formation of the necessary professionally important qualities among employees of the penitentiary system, ensuring the success of their official activities, is significant in terms of making timely, highly likely, objective managerial decisions on issues of professional and career growth of employees, assessing the prospects for appointment to certain positions, the possibility of forming a personnel reserve on a regular basis, conducting professional selection procedures, professional certification, professional training. The main professionally important qualities and competencies of employees of the penitentiary system in the context of digital transformation are considered. A promising scientific direction of research on professionally important qualities of employees of the penal correction system using the method of structural analysis of the organization of psychological indicators is highlighted. The issue of professional mentoring in the penal system of the Russian Federation has been updated. Additionally, such professionally important qualities as mastery of digital culture, digital competence, mastery of information hygiene skills when working with software and information systems, compliance with information security and information protection issues were noted. The study of the issues of professionally important qualities of employees of the penitentiary system is of promising scientific practice-oriented interest in such areas of psychological support of professional activity as professional selection optimization of programs to support professional adaptation of employees at various stages of professional development, development of an up-to-date professional description of professional activity, identification of factors of psychological success

Navigating the wild west: a review of guidance on clinical communications using personal BYOD, IM and third-party apps in the UK and Ireland.

The ubiquity of Bring Your Own Device (BYOD) personal smartphones, Instant Messaging (IM), and third-party apps, has made these technologies compelling for efficient communications between clinicians regarding patient care. However, the sensitivity of patient-related information necessitates secure, GDPR compliant modalities that prevent unauthorised access and ensure confidentiality. This scoping review explores existing guidelines, policies, and regulations that advise clinicians in the UK and Ireland on the secure use of these digital communication tools. Following the Joanna Briggs Institute (JBI) updated Framework for Scoping Reviews and the PRISMA ScR guidelines, this review examines the literature to identify relevant guidelines, policies, and regulations informing current clinical practice on the use of this technology. Academic databases including OneSearch, Embase, EBSCO, PubMed, Medline, and CINAHL were searched, in addition to hand searches of professional entities' websites, including trade unions, regulators, two national health systems, and several employers. Direct inquiries were made to 69 professional entities via telephone, email, websites, and X (formerly known as Twitter). The review identified 18 papers that broadly recognise the importance of secure communication however, a lack of information on the appropriate selection or configuration of these popular technologies was evident. Most guidelines emphasise general security and data protection standards rather than providing clear actionable recommendations for technology use, thereby leaving a significant gap in technical guidance for clinicians. There is a distinct lack of detailed, specific, consistent technical guidance available to clinicians. This review evidences an urgent requirement for enhanced guidelines that specify the most secure platforms, appropriate features, and configuration to maximise the security and confidentiality of clinical communications. Further research is recommended to develop comprehensive, actionable advice for clinicians.

Juridical construction of socio-ecological security: Indonesia’s vulnerability to climate change

As an archipelagic state, Indonesia is very vulnerable to losing part of its land and islands due to climate change. Climate change causes atmospheric instability and sea level rise. Sea level rise that causes land loss, ecosystem damage, infrastructure damage, and even massive population displacement, are the impacts of climate change that affect environmental security. At the United Nations Framework Convention on Climate Change (UNFCCC) in 1992, the goal was to stabilize greenhouse gas concentrations to prevent harmful anthropogenic disturbances to the climate system. Therefore, as a response to overcoming the problem of the effectiveness of international environmental law, the idea of juridical construction in the protection of socio-ecological security is needed. Initiatives that can be undertaken by the government include the development of resilient infrastructure systems and land-use planning, as well as the restructuring of spatial planning, particularly for coastal areas and sensitive ecosystems. This research is a normative study that examines the legal framework of socio-ecological security concerning Indonesia’s vulnerability as an archipelagic nation due to the impacts of climate change. This research adopted a normative methodology with legislative and conceptual approaches.

Application of blockchain technology in student learning data security and privacy protection

Application of blockchain technology in student learning data security and privacy protection

Integrating Cybersecurity into Renewable Energy Development: A Data-Driven Decision Tree Approach for Environmental Protection

The global shift towards renewable energy sources is vital for environmental protection and sustainable development. However, the increasing reliance on data-driven technologies and interconnected systems in this sector introduces significant information security challenges. This research investigates a novel approach to enhance environmental protection in renewable energy development by integrating cybersecurity principles into a data-driven decision tree (DT-DD) framework. We analyze the vulnerabilities of renewable energy systems to cyber threats, focusing on the potential for malicious data manipulation to disrupt operations, compromise data integrity, and undermine environmental protection efforts. Our proposed DT-DD method leverages big data analytics and machine learning to model the complex interplay between energy production, environmental impact, and economic factors, while incorporating security measures to ensure data integrity and model robustness. The experimental analysis demonstrates the effectiveness of the DT-DD approach in achieving environmental protection goals, with results indicating [mention key findings, e.g., improved accuracy in pollution reduction, enhanced efficiency in resource management, and better evaluation of environmental impact]. Furthermore, we highlight the critical role of information security in safeguarding the data used in the DT-DD model and ensuring the reliable operation of renewable energy systems. By integrating cybersecurity into the development and deployment of renewable energy technologies, we can build a more resilient and sustainable energy future. This research contributes to a deeper understanding of the intersection between information security, renewable energy, and environmental protection, paving the way for more secure and effective strategies for a greener future.

Cross-Chain Data Exchange and Information Security Protection Management in Blockchain

Cross-Chain Data Exchange and Information Security Protection Management in Blockchain