Security Model Research Articles

Deterministic Systems for Cryptographic Primitives Used in Security Models in Particular IoT Configurations

Computing systems grouped in subnets use distributed security models, in general, by creating session keys based on the Diffie–Hellman model, and calculating the necessary parameters for this, on each of the systems. In the particular case of a network of devices heterogeneous in terms of computing power, such as IoT, the modeling of a security system of the entire structure will have to take into account the fact that some devices have a very low computing power. In this sense, starting from the study of some general models, used in structures of this type, an integrated structure was developed to secure communications and test certain vulnerable components, to calculate a degree of risk that they are maliciously intended. The system was developed with a customized mathematical model, a scheme for propagation and management of cryptographic parameters and a test in a real environment by creating the algorithmic model and implementing it within a structure of a beneficiary.

Analysing how AI-powered chatbots influence destination decisions.

This study aims to explore the role of destination chatbots as innovative tools in travel planning, focusing on their ability to enhance user experiences and influence decision-making processes. Based on the Technology Acceptance Model, Enterprise Content Management, and Information Systems Security models, the study examines the psychological, emotional, and technological factors that drive user satisfaction, continued use, and intention to visit a destination. Understanding these factors is crucial for improving chatbot design and optimizing their implementation in the tourism industry. A total of 312 responses were collected from university students who regularly engage in tourism-related activities. The survey employed a structured questionnaire with items measuring information quality, user satisfaction, perceived enjoyment, usefulness, and behavioral intentions using a 7-point Likert scale. Structural equation modelling [SEM] was used to analyze the relationships between constructs, allowing us to evaluate the validity and reliability of the model. The results reveal that information quality positively enhances user satisfaction, perceived enjoyment, and perceived usefulness. Moreover, perceived enjoyment and usefulness are critical psychological and emotional drivers influencing users' decision to continue utilizing chatbots. Additionally, the analysis highlights the intention to continue using destination chatbots as a strong predictor of tourists' intention to visit the destination. The findings contribute to the theoretical understanding of technology acceptance and user behavior in tourism, while providing practical insights for destination managers and developers to enhance chatbot features and improve traveler engagement.

Owner Bot: Secure Cloud Data Management Using Blockchain and Homomorphic Encryption

Cloud storage services have gained significant popularity due to their fundamental support for the rapid growth of cloud computing. However, despite their advantages, security risks such as data leakage, mismanagement, and malicious attacks still persist. This project proposes a security model that combines cloud computing with blockchain technology to ensure data integrity while maintaining confidentiality through homomorphic encryption.

Optimization of the Decision-making Process of Digital Twins in Network Security Based on Graph Neural Networks

Optimization of the Decision-making Process of Digital Twins in Network Security Based on Graph Neural Networks

An adaptive machine learning model for IoT security to detect spoofing and DOS attacks

The rising number of Internet of Things (IoT) devices necessitates robust security to counter threats like Denial-of-Service (DoS) and spoofing attacks. Many Intrusion Detection Systems (IDS) leverage Machine Learning (ML), but challenges like concept drift and class imbalance persist. This study presents an adaptive ML-based IDS framework using a Modified XGBoost (Mod-XGB) model. Mod-XGB incorporates a weighted loss function for class imbalance, adaptive instance weighting for concept drift, and a security penalty term to enhance feature selection. Evaluated on the CICIoV2024 dataset, Mod-XGB achieved 97.96% accuracy, outperforming models like Random Forest, Logistic Regression, and AdaBoost. This innovative approach effectively addresses IoT security challenges, offering significant advancements in IDS performance for improved attack detection and resilience in dynamic environments.

The Evolution of Backup Security: Integrating Zero Trust, Encryption, and Access Control for Data Integrity

With the increasing frequency and sophistication of cyber-attacks, ensuring the security of backup data has become a critical concern for organizations worldwide. Traditional backup security methods, often reliant on perimeter defenses and basic encryption, are no longer sufficient in the face of advanced persistent threats, insider risks, and evolving regulatory requirements. This research explores the evolution of backup security, focusing on the integration of three key security measures: Zero Trust, encryption, and access control. The Zero Trust security model, which operates on the principle of "never trust, always verify," offers a robust framework for minimizing unauthorized access and insider threats. Encryption ensures the confidentiality and integrity of backup data, both at rest and in transit, while access control mechanisms enforce the principle of least privilege to restrict access to sensitive backup information. By integrating these components, organizations can enhance the security and integrity of their backup systems, ensuring that critical data is protected against a wide range of cyber threats. This paper also discusses the challenges of implementing these security measures, including technical, organizational, and regulatory hurdles, and highlights future directions for securing backup environments in the age of cloud computing and AI-driven security tools. Keywords: Backup Security, Zero Trust, Encryption, Data Integrity, Access Control, Disaster Recovery, Insider Threats, Role-Based Access Control (RBAC), Least Privilege, Cybersecurity, Data Protection, Cloud Security, Backup Integrity, Authentication, Security Frameworks, Cyber Threats.

Zero Trust AI Authentication and Blockchain Powered Secure AI

The integration of Zero Trust (ZT) security models with Artificial Intelligence (AI) authentication mechanisms, along with the utilization of blockchain technology, offers a novel paradigm for securing digital interactions and data exchanges in increasingly decentralized and complex networks. Zero Trust, a security framework that assumes no implicit trust and enforces strict identity verification, is well-suited for AI-driven authentication systems that require robust, real-time, and adaptive security measures Blockchain technology further enhances this framework by providing a transparent, immutable ledger for logging authentication events, access control decisions, and transactions, ensuring tamper-proof audit trails. Blockchain’s decentralized nature also mitigates single points of failure, improving resilience and privacy. When combined, Zero Trust, AI, and blockchain can deliver an advanced, self-evolving security system that both anticipates and responds to new threats with greater precision and efficiency.

Study on the Application of Dynamic Continuous Trust Assessment Model Based on Clustering Mechanism in Network Security Optimization

Due to the rapid development of network technology and the increasing size of networks, the security of networks in today’s society is facing great challenges. The study of network security is based on the study of network security frameworks, connections, and models to improve network security so that the network becomes controllable, manageable, and survivable. In this paper, we propose a dynamic continuous trust assessment model based on the clustering mechanism and apply this model to the network security model optimization problem. The specific conclusions are as follows: (1) The problems in previous clustering algorithms are analyzed, and corresponding solutions are proposed for the problems. Then, the particle swarm algorithm is briefly introduced, the inertia weight coefficients in the particle swarm algorithm are improved, and the dormant mechanism of neighboring node groups is introduced. (2) A new algorithm for calculating direct trust value is proposed. (3) A method for evaluating the overall trust value level of the network is proposed. The method first selects the N nodes with the highest number of interactions and then evaluates the overall trust level of the network in which these N nodes are located by using the associative memory capability of the clustering algorithm. (4) The experimental analysis shows that the randomly generated rule set in the model of this paper tends to evolve to a stable state after 60∼80 rounds of operation, with a higher success rate, and at 180∼200 rounds of operation, the rule set evolves to a stable state again, with the success rate still improved; after 240 rounds of operation, the network environment is restored to the initial state, and although the roles of some of the nodes are changed, the model still evolves to a high level again. Level. The dynamic continuous trust evaluation model proposed in this paper can be effective in the network security optimization, but the practical application of the model should be done further research.

Seeking Indonesia's Food System Concept: Drawing Lessons from Food Estate Project

Efforts to overcome food insecurity are becoming more crucial as triggered by the combination of climate change, global conflicts, rising cost of living, declining food production and high food prices. Indonesia with population growth, which is projected to increase by 69, 1% in 2045 or up to 311 million people in 2050, need to provide solution to reduce and mitigate the impact of food insecurity. Indonesia’s heavy reliance on economic agriculture, fisheries, forestry and other natural resources, often troubled by all situations related to climate-related hazards. The government is trying to address problems with the food estate project, but has elicited backlash from some scholars and CSOs, as the project neglects socio-economic and cultural dimensions of local people. The opponents argue that the project is derived from neoliberal food security model, which has proven failed to eradicate the hunger problem. Researcher is using qualitative method with a desk study approach to examine data and information taken from secondary data sources. Researcher revealed some factors that made the food estate did not work as expected. One of them is due to the concept that ignores the importance of cultural diversity and ecologies coupled with a lack of engagement with the local population. The researcher provides some evidence on the weakness of the food security approach, and offer a policy shift to a mixed approach that fits the Indonesian case.

Zero-Trust Security Models for Cloud Data Analytics: Enhancing Privacy in Distributed Systems

The abstract is typically the first item that interacts deeply with a reader of academic and scientific literature to point out the contributions and contextualize the importance of the subject, indicating current problems and future lines that might be addressed by the implementation and evaluations

The Effective Integration of Multi-Factor Authentication (MFA) with Zero Trust Security

As many organizations face the rise in cyber threats, our digital landscape demands a more vigorous network. This paper explores the effectiveness of integrating Multi-Factor Authentication (MFA) within the popular Zero Trust security model by using a collection of case studies (qualitative analysis) combined with known security breaches (quantitative analysis) as a means of identifying key strategies in determining user authenticity while strengthening trust boundaries. The findings indicate that a comprehensive collaborative approach is necessary when implementing MFA. This approach should integrate real-time enforcement of security policies, leveraging dynamic threat intelligence and situational information to effectively decrease unauthorized access and prevent data breaches. The study concludes with recommendations for implementing MFA as an essential component of Zero Trust architecture. It emphasizes continuous verification while using access control through IT policies for administrators to control user access based on multiple real-time factors. This integration strengthens security postures while maintaining alignment with regulatory compliance standards.

Digital Innovation in Admission Processes based on an Architectural Model of Information Security using Blockchain Technology

INTRODUCTION: The digital transformation in higher education, particularly in admission processes, has raised concerns about data integrity and security. In Peru, public universities face challenges in ensuring transparency and protecting sensitive information during admissions. This research proposes an innovative solution using BLOCKCHAIN technology to enhance data security and integrity. OBJECTIVES: The study aims to develop a digital innovation proposal based on an architectural model of information security, specifically to (1) ensure data integrity and confidentiality, (2) propose a secure online admission process, and (3) analyze current admission processes (AS-IS) while designing an improved model (TO-BE) with BLOCKCHAIN technology. METHODS: This study follows a quantitative approach, aiming to implement digital innovation through an architectural information security model to ensure data integrity in admission processes. To achieve this, BLOCKCHAIN technology is applied, known for its ability to securely record transactions and enhance data transparency and security. A proposed eight-phase process was then developed, focusing on an architectural model for secure information handling, incorporating BLOCKCHAIN for data transparency. RESULTS: The proposed model significantly improves data integrity and security, ensuring transparency and reducing fraud risks. The TO-BE process addresses vulnerabilities in the current system, offering a more efficient framework for managing sensitive data throughout the admission phases. CONCLUSION: Public universities in Peru must adopt BLOCKCHAIN technology to safeguard data integrity in admissions. The proposed model offers a robust solution for secure and reliable online processes, with potential for a transformative impact.

Cloud security modeling: using TCP deltas with data analytics and machine learning techniques

Cloud security modeling: using TCP deltas with data analytics and machine learning techniques

Zero Trust and Cloud Identity: Building a Resilient Security Framework

This article explores the implementation of Zero Trust Architecture (ZTA) and cloud identity solutions in building resilient security frameworks for modern enterprises. As traditional perimeter-based security models become inadequate, organizations are shifting towards identity-centric approaches that incorporate continuous verification and least privilege access principles. It examines the evolution of identity management, emphasizing the transformation towards decentralized identity systems and their integration with established security frameworks. Through analysis of current standards and best practices, including NIST guidelines and industry frameworks, the article presents comprehensive strategies for implementing Zero Trust principles alongside modern Identity and Access Management (IAM) solutions. It encompasses critical components such as identity governance, role-based access control, micro-segmentation, and automated policy enforcement. The article also addresses implementation challenges, providing a phased approach for organizations transitioning to Zero Trust Architecture while maintaining operational efficiency. Additionally, the article explores emerging trends and preparation strategies, offering insights into future considerations for maintaining robust security postures in an evolving threat landscape.

Simplifying Attribute-Based Access Control (ABAC) for Modern Enterprises

This article provides a comprehensive exploration of Attribute-Based Access Control (ABAC) and its growing importance in modern enterprise security. It begins by explaining the core principles and components of ABAC, contrasting it with traditional Role-Based Access Control (RBAC) to highlight its advantages in flexibility, granularity, and context-awareness. The article then delves into the practical aspects of implementing ABAC in organizations, discussing key steps such as assessing current systems, defining attributes, developing policies, and managing the transition. Real-world examples illustrate ABAC's application in securing cloud-native applications and managing access for dynamic workforce scenarios. The article also addresses challenges associated with ABAC implementation, including policy complexity, performance considerations, legacy system integration, and privacy concerns. Finally, it examines future trends in ABAC, such as its integration with artificial intelligence and machine learning, its role in zero-trust security models, and ongoing standardization efforts. Throughout, the article emphasizes ABAC's potential to revolutionize access management by enabling more nuanced, adaptive security policies that can better align with the complexities of modern enterprise environments.

Intrusion Detection System for Network Security Using Novel Adaptive Recurrent Neural Network-Based Fox Optimizer Concept

The majority of daily networks and communications rely heavily on network security. Researchers in cybersecurity emphasize the necessity of developing effective intrusion detection systems (IDS) to safeguard networks. The importance of efficient IDS escalates as attackers devise new types of attacks and network volumes expand. Furthermore, IDS aims to ensure the integrity, confidentiality, and availability of data transmitted across networked systems by preventing unauthorized access. Following numerous studies utilizing machine learning (ML) to develop effective IDS, the focus has shifted towards deep learning (DL) techniques as artificial neural networks (ANNs) and DL systems have become prevalent. ANNs are capable of generating features autonomously, eliminating the need for manual intervention. This paper introduces an innovative adaptive recurrent neural network-based fox optimizer (ARNN-FOX) method. The primary objective of the ARNN-FOX system is to efficiently detect and classify network intrusions, thereby enhancing network security. Data normalization is conducted to scale the incoming data into a usable format. The gray level co-occurrence matrix (GLCM) method is proposed for selecting the optimal subset of features for the ARNN-FOX method. In the proposed approach, the fox algorithm (FOX) is utilized for the adjustment of hyperparameters in the ARNN model. The efficacy of the ARNN-FOX approach is assessed using benchmark datasets. Based on comparative results, the ARNN-FOX method demonstrates superior performance in parameters such as accuracy, specificity, sensitivity, F1 Score, recall value, and precision values over existing models. The proposed ARNN-FOX-based IDS model for the network security in terms of accuracy is 15.12%, 8.79%, 6.45%, and 4.21% better than RNN, CNN-LSTM, DASO-RNN, and ChCSO-LSTM, respectively. Similarly, with respect to specificity, the suggested ARNN-FOX-based IDS model for network security outperforms RNN, CNN-LSTM, DASO-RNN, and ChCSO-LSTM by 32.43%, 8.89%, 3.16%, and 2.08%, respectively.

From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications

Background/Objectives: This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience and practical applications. Methods: Following PRISMA, 64 studies were reviewed that outlined the latest feature extraction and classification developments using deep learning models such as CNNs and RNNs. Results: Indeed, the findings showed that the multimodal approaches were practical, especially the combinations involving EEG with physiological signals, thus improving the accuracy of classification, even surpassing 90% in some studies. Key signal processing techniques used during this process include spectral features, connectivity analysis, and frontal asymmetry detection, which helped enhance the performance of recognition. Despite these advances, challenges remain more significant in real-time EEG processing, where a trade-off between accuracy and computational efficiency limits practical implementation. High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. Aside from this, the significant obstacles are inconsistency in labeling emotions, variation in experimental protocols, and the use of non-standardized datasets regarding the generalizability of EEG-based emotion recognition systems. Discussion: These challenges include developing adaptive, real-time processing algorithms, integrating EEG with other inputs like facial expressions and physiological sensors, and a need for standardized protocols for emotion elicitation and classification. Further, related ethical issues with respect to privacy, data security, and machine learning model biases need to be much more proclaimed to responsibly apply research on emotions to areas such as healthcare, human–computer interaction, and marketing. Conclusions: This review provides critical insight into and suggestions for further development in the field of EEG-based emotion recognition toward more robust, scalable, and ethical applications by consolidating current methodologies and identifying their key limitations.

54 STUDY ON THE HEALTH INTERVENTION OF MENTAL DISORDER GROUPS WITH THE HELP OF MEDICAL SERVICE FINANCED BY LOCAL SOCIAL SECURITY

Abstract Background The financing model of local social security directly affects the accessibility and equity of medical services, which is of great significance to the health status of the vulnerable group with mental disorders. People with mental disorders often face multiple challenges such as inadequate medical resources, low adherence to treatment, and lack of social support, resulting in low overall health. In recent years, the innovation of social security financing mechanisms in various regions has provided stable financial support for medical services and provided possibilities for improving the health status of patients with mental disorders. However, there is still little research on how local social security financing affects the health of people with mental disorders. The purpose of this study is to explore the intervention effect of medical services financed by local social security on the health of groups with mental disorders, and to provide scientific basis for optimizing resource allocation and policy formulation. Methods A mixed study design was adopted in this study. A total of 240 patients with mental disorders who participated in the local social security financing mechanism in a certain region were selected as research objects and randomly divided into the intervention group and the control group (120 patients each). The intervention group received intensive medical service intervention through local social security financing support, including whole-course medical care, specialist diagnosis and treatment services, and community support programs. The control group received routine medical care. The study lasted 12 months and assessed mental health, functional recovery and quality of life at baseline, 6 months and 12 months. The main measurement tools include the General Health Questionnaire, GHQ-28), the World Health Organization Quality of Life-Brief Version (WHOQOL-BREF), combined with in-depth interviews to understand patients’ experience of healthcare services. The data were analyzed by repeated measure ANOVA and structural equation model. Results The total GHQ-28 score in the intervention group decreased significantly after 12 months, from 18.5±4.2 to 11.3±3.7, which was significantly better than that in the control group, from 18.4±4.1 to 15.8±3.9, at which time P<0.05. WHOQOL-BREF scores also improved significantly in the intervention group, with total health area scores rising from 49.2±7.5 at baseline to 68.7±8.3 at 12 months (P<0.05). The results of in-depth interviews showed that patients’ satisfaction with specialist diagnosis and treatment services was significantly improved, and the community support program effectively improved patients’ social functioning and emotional state. Discussion The study showed that medical services supported by local social security financing significantly improved the health status and quality of life of patients with mental disorders, especially in alleviating symptoms, promoting functional recovery and improving patient satisfaction. This mechanism provides a reliable financial guarantee and innovative service model for the health intervention of mental disorder groups. Future studies can further explore the long-term effects of different financing models, and combine economic assessment to optimize the efficiency of resource allocation and promote more scientific and precise policy design.

Future-Proofing Enterprise Security: Transitioning Legacy Authentication to Modern IAM

As enterprises undergo digital transformation, legacy authentication systems pose significant security and operational challenges. These outdated authentication mechanisms lack adaptive security controls, multi-factor authentication (MFA), and compliance readiness, making them vulnerable to credential-based attacks, unauthorized access, and identity fraud. With the rise of Zero Trust security models, AI-driven authentication, and passwordless authentication, organizations must transition to modern Identity and Access Management (IAM) frameworks to enhance security, scalability, and compliance. This paper explores the risks associated with legacy authentication systems, evaluates modern IAM solutions, and presents a strategic migration roadmap. It examines key authentication standards such as OAuth 2.0, OpenID Connect (OIDC), Security Assertion Markup Language (SAML), Fast Identity Online (FIDO2), and risk-based adaptive authentication. A case study highlights the successful migration of a global enterprise from outdated IAM protocols to modern authentication standards, demonstrating improved security posture, reduced attack surface, streamlined user experience, and compliance with frameworks such as NIST, GDPR, HIPAA. The study concludes with recommendations for futureproofing IAM strategies using AI-driven identity governance, continuous authentication, and Zero Trust principles.

A New Secure Model for Cloud Environments Using RBFNN and AdaBoost

A New Secure Model for Cloud Environments Using RBFNN and AdaBoost