Role-based Access Control Research Articles

Towards a transformation approach from SecureUML to ‎Alloy

In critical information systems, Role-Based Access Control (RBAC) has been frequently used. The specification of access control policies is an essential step in securing information systems. This article presents a transformative approach for addressing design challenges within these specifications using Model-Driven Architecture (MDA). A method is introduced for specifying and verifying model-based access control (RBAC) rules by combining SecureUML and Alloy, referred to as SecureUML2Alloy. This approach leverages transformation techniques based on metamodels to express the SecureUML source model in the Alloy target model. The SecureUML2Alloy framework allows for the generation of an initial Alloy code that can be extended with facts, functions, and predicates depending on the case. The transformative framework offers a reliable, automated mechanism for verifying security specifications in critical systems, promoting both theoretical and practical advancements in model transformation and formal verification. This method provides a pathway for future automation tools in secure system design and analysis, contributing to the reliability and security of software systems across multiple fields.

Design and implementation of anti-mapping security access technology based on illegal scanning

Abstract In the current field of information security, illegal network scanning activities are prevalent, and such behaviors are usually aimed at detecting security vulnerabilities in network systems and preparing for future attack activities. This study proposes a secure access system based on anti-mapping technology, which aims to effectively block illegal scanning behaviors while ensuring that the normal access of legitimate users is not affected. The system integrates advanced behavioral analysis algorithms that utilize machine learning techniques for deep learning and pattern recognition of network traffic, and is able to accurately distinguish between normal user activities and malicious scanning attempts. At the core of the system is a set of dynamic adaptive identification mechanisms that update the detection algorithms in real time to adapt to emerging scanning techniques and attack strategies by continuously learning from changes in network traffic. In addition, the system employs role-based access control (RBAC) policies to enhance the protection of sensitive resources. The Secure Access Gateway is deployed at the boundary of the network to monitor and filter all ingress traffic, effectively intercepting unauthorized scanning activities by comprehensively evaluating the source, behavior and frequency of traffic. Experimental results show that the proposed two-layer network structure performs well in detecting common threats such as port scanning, DDoS attacks, and SQL injections, with an accuracy rate of over 95%. Especially for complex and covert APT (advanced persistent threat) attacks, the system can significantly reduce the false alarm rate and effectively improve the detection speed. However, when dealing with some highly customized malware, the system's recognition ability still needs to be improved, which indicates that future research needs to focus more on enhancing the ability to learn and adapt to unknown threats.

Block Medcare: Advancing Healthcare Through Blockchain Integration

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as integration with IoT devices and AI-driven analytics, this research contributes to the ongoing evolution of secure, efficient, and interoperable healthcare systems.

Combining Pretty Good Privacy and Role-Based Access Control Technologies for Access Protection to Confidential Data

Combining Pretty Good Privacy and Role-Based Access Control Technologies for Access Protection to Confidential Data

The Critical Role of Role-Based Access Control (RBAC) in securing backup, recovery, and storage systems

In the contemporary digital landscape, safeguarding data through robust access control mechanisms is crucial. Role-Based Access Control (RBAC) is an effective strategy for managing access to backup and recovery systems, as well as storage devices. RBAC assigns permissions based on user roles, thereby enforcing the principle of least privilege and enhancing data security. This blog explores the significance of RBAC in these contexts and extends its relevance to ransomware and malware threats, as well as disaster recovery. Supported by scientific research and industry case studies, it underscores how RBAC can mitigate risks, ensure regulatory compliance, and improve data management practices.

Insights from ARCOS-V’s Transition to Remote Data Collection during the COVID-19 Pandemic: A Descriptive Study

Introduction: The ARCOS-V study, an epidemiological study on stroke and transient ischaemic attack (TIA), faced the challenge of continuing data collection amidst the COVID-19 pandemic. This study aimed to describe the methodological changes and challenges encountered during the transition from paper-based methods to digital data collection for the ARCOS-V study and to provide insights into the potential of using digital tools to transform epidemiological research. Methods: The study adapted to remote data collection using REDCap and Zoom, involving daily health record reviews, direct data entry by trained researchers, and remote follow-up assessments. The process was secured with encryption and role-based access controls. The transition period was analysed to evaluate the effectiveness and challenges of the new approach. Results: The digital transition allowed for uninterrupted monitoring of stroke and TIA cases during lockdowns. Using REDCap and Zoom improved data reach, accuracy, and security. However, it also revealed issues such as the potential for systematic data entry errors and the need for robust security measures to protect sensitive health information. Conclusion: The ARCOS-V study’s digital transformation exemplifies the resilience of epidemiological research in the face of a global crisis. The successful adaptation to digital data collection methods highlights the potential benefits of such tools, particularly as we enter a new age of artificial intelligence (AI).

HEALTHSOLID 4.0: A novel solid-pod and blockchain-enabled framework for role-based access control and secure healthcare information exchange

HEALTHSOLID 4.0: A novel solid-pod and blockchain-enabled framework for role-based access control and secure healthcare information exchange

Optimizing Identity Management: Key Strategies for Effective Governance and Administration

The significance of identity management has escalated in today's digital environment, where sensitive information is frequently at risk of breaches and unauthorized access. This research aims to investigate the best governance and administrative practices to enhance identity management systems, with a focus on security, privacy, and usability. By analyzing current industry standards, regulations, and technological advancements, the study intends to provide valuable insights for organizations seeking to improve their identity management capabilities. The research employs a mixed-methods approach, combining quantitative surveys and data analysis with qualitative interviews, to achieve a comprehensive understanding of current practices and challenges in identity governance and administration. Key components such as authentication, authorization, and access control are examined, with practical recommendations provided to enhance identity management strategies. The study emphasizes the importance of adopting role-based access control (RBAC), continuous monitoring and compliance, identity lifecycle management, and integrating identity governance with IT infrastructure. Additionally, it highlights the significance of effective password management, robust authentication measures, and the implementation of Single Sign-On (SSO) solutions to improve security and user experience. The research also underscores the critical role of data encryption and protection measures in safeguarding sensitive information and mitigating data breach risks. By adhering to best practices in identity management, organizations can strengthen their overall cybersecurity posture, ensure regulatory compliance, and build trust among stakeholders in an increasingly complex digital landscape.

Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks

Internet of Things (IoT) has the potential to significantly impact various domains e.g. health, transportation, automation, and emergency response to both man-made and natural disasters, particularly in scenarios where human decision is challenging. In this research a Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks has been proposed. This framework ensures robust security and optimized data handling through granular access control mechanisms based on predefined roles. By leveraging RBAC, it mitigates unauthorized access risks, thereby safeguarding sensitive IoT data during transmission and storage. Our approach emphasizes efficiency by streamlining data processing workflows, reducing latency, and optimizing resource utilization. The framework is designed to scale with IoT network expansions and adapt to evolving security needs, promising enhanced reliability and trustworthiness in data operations for contemporary IoT environments. According to this research work current security effectiveness is 99 percent.Home area network (HAN) can be used for smart connectivity of different home appliances using IoT and automatic start and stop feature may be possible.Access control server (ACS) is used to control access and provide permission for different operations.

Innovative Solutions for High School Inventory Management: An Information System Approach

This paper explores the development and implementation of an innovative information system designed to enhance inventory management in high schools. Traditional inventory methods, often reliant on manual tracking, are prone to errors and inefficiencies, which can hinder resource allocation and decision-making. By adopting an information system approach, schools can automate processes, improve data accuracy, and provide real-time access to inventory information. The study outlines key features such as real-time tracking, automated alerts, and role-based access controls, which are critical to the system's effectiveness. The results demonstrate significant improvements in workflow efficiency, data accuracy, and overall resource management. Despite the challenges associated with system implementation, including cost and training requirements, the benefits make this approach a compelling solution for modernizing high school inventory practices. The paper concludes with recommendations for broader adoption and further research to optimize these systems for educational settings.

Data Protection in Healthcare Information Systems Using Cryptographic Algorithm with Base64 512 bitsAgnes

Recent advancement in technology in Healthcare Information Systems has led to a rise in cyber-attacks and data leakages at the data level. Existing data-level protection techniques that are developed to ensure data-level protection in Healthcare Information Systems lack integration of key security models such as Mandatory Access Controls, Role Based Access Controls and database security approaches in the design and development of data protection methods. In this study, science research methodology was used to design and develop a cryptographic algorithm with Base64 512 bits to enhance data protection at the data level. The algorithm was tested in a healthcare information system through experiments and simulations. The performance of the algorithm was tested to evaluate encryption and decryption process, strength on brute force attack and plain text vulnerability. The results of the observations showed that the developed algorithm with Base64, AES with fixed length of 512 bits, achieved optimal performance.

Cloud-Based Access Control Including Time and Location

Location-based services (LBS) offer various functionalities, but ensuring secure access to sensitive user data remains a challenge. Traditional access control methods often need more detail to enforce location-specific restrictions. This paper proposes a new approach that utilizes the Generalized Spatio-Temporal Role-Based Access Control Model (GSTRBAC) within the context of LBS. GSTRBAC grants access based on user credentials, authorized locations, and access times, providing a detailed approach to securing LBS data. We introduce an optimized cloud-based GSTRBAC implementation suitable for deployment in modern LBS architectures. The system uses two secure communication protocols tailored to different security requirements. This allows for efficient communication for less-sensitive data while offering robust protection for highly sensitive information. Additionally, a proof-of-concept mobile application demonstrates the system’s functionality and efficiency within an LBS environment. Our evaluation confirms the effectiveness of the cloud-based GSTRBAC implementation in enforcing location-specific access control while maintaining resource and time efficiency.

Leveraging Machine Learning to Improve Access Control Mechanisms in Data Warehousing

This paper delves into integrating Machine Learning (ML) algorithms into Access Control Mechanisms (ACM) within Data Warehouses (DW) to enhance both security and operational efficiency. Traditional ACMs, notably role-based access control (RBAC), often struggle with adapting to dynamic threats and complex access patterns. ML tackles these issues by improving access decision accuracy through real-time anomaly detection and adaptive control based on user behavior and contextual insights. The study examines the implementation and benefits of ML-enhanced ACMs in DW environments, highlighting their effectiveness in mitigating unauthorized access and insider threats. This research also addresses critical considerations, such as privacy protections and the interpretability of ML models, crucial for maintaining regulatory compliance and stakeholder trust. Ultimately, this study emphasizes ML's pivotal contribution to advancing security practices within Data Warehouses (DWs) and proposes future research directions to enhance adaptive security measures in evolving digital landscapes.

Resiliency Analysis of Role-Based Access Control via Constraint Enforcement and Mathematical Programming

Resiliency Analysis of Role-Based Access Control via Constraint Enforcement and Mathematical Programming

ABC REAL ESTATE MANAGEMENT SYSTEM

Abstract— This thesis presents the development and implementation of the ABC Real Estate Management System, a web-based application built using PHP and MySQL. The system is designed to streamline property management for real estate agencies, property managers, and individual landlords. It offers a user-friendly interface that caters to two primary user groups: general users and administrators. Users can search for properties based on location and features, utilize a loan calculator for financial planning, and express interest in properties. Administrators have comprehensive control over the system, including managing user accounts, property listings, and potentially real estate agents. The system emphasizes three key aspects: improved user experience, enhanced efficiency, and increased security. User-friendliness ensures that both users and admins can interact with the system intuitively. Efficiency is achieved through quick property discovery and streamlined property management tasks. Security is reinforced by implementing role-based access control, ensuring that only authorized users can access and modify sensitive data. By leveraging PHP and MySQL, the ABC Real Estate Management System addresses common challenges in property management, fostering a more efficient, secure, and user-friendly experience for both property seekers and managers. This thesis provides a detailed analysis of the system’s design, development, and potential impact on the real estate industry. Keywords: Real estate management system (REMS), Efficiency in property management, Streamlining property operations.

Real Estate Website

Abstract— This paper presents a novel real estate management system designed to address the inefficiencies faced in the contemporary property market. Developed using PHP and MySQL, the system offers a user-friendly web application that streamlines the processes for both property seekers and managers. Catering to distinct user groups, it empowers users with a comprehensive property database searchable by location and desired features. Additionally, users can leverage a built-in loan calculator (depending on implementation) for financial planning and potentially express interest in suitable properties. Admins, on the other hand, benefit from comprehensive management functionalities. These include user account management, property listing creation, editing, and availability control. The system also offers the potential to manage real estate agents, further enhancing its functionality for larger organizations. This user-centric approach prioritizes a user-friendly interface that simplifies searching, listing management, and overall interaction with the system. To ensure data security and integrity, the system implements robust role-based access control, restricting access to sensitive information based on user roles. Furthermore, a powerful search function facilitates efficient property discovery, allowing users to find their ideal properties with minimal effort. This paper delves into the details of the system, outlining its functionalities, the chosen development methodology (e.g., Agile), and the software and hardware requirements for running the application. It also acknowledges the system’s limitations in its initial phase, such as the potential need for future expansion of search filters or communication tools. Finally, the paper explores the exciting future scope of the system, including advanced search capabilities, integrated communication features, mobile application development, and potential integration with smart home technologies and machine learning for personalized recommendations. Keywords – Real Estate Management System, Property Management System, PHP,MySQL,User-Friendly Interface

Policy-Based Contextual Access Control Mechanism for Smart IoT Devices

Access control remained an important aspect of computer security, and it has been the focus of extensive research over the past several decades. Access control mechanisms generally composed of two fundamental components: authentication and authorization. Authentication refers to verifying the identity of an entity, and authorization guarantees that only authenticated entity or devices can access the permitted devices or other resources. Various traditional access control schemes, such as Access Control Lists (ACLs), Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC) exist, but these have certain limitations that hinder their direct implementation in the Internet of Things (IoT). For instance, ACLs maintain user-specific access privilege lists, which are feasible for environments with limited users and devices, but impractical for large scale systems like IoT. RBAC assigns devices access through roles associated with permissions, however role management in dynamic IoT environments poses significant challenges. ABAC grants access based on user and devices attributes that requires certain attribute criteria for authorization. We advocate that IoT environments are dynamic in nature and consist of very large volumes of smart IoT devices (such as smart sensors, smart phones and gadgets) that which introduce unique access control challenges. One significant challenge is providing dynamic access to smart IoT devices, as opposed to relying on static rules, roles, or attributes. Considering these challenges, this research advocates for a novel access control scheme tailored for accessing smart IoT devices in internet of things environments. The prototype implementation of the proposed approach is carried out along with conducting the usability study to evaluate the performance and suitability of the proposed system for real world internet of things (IoT) scenarios.

Fog-Assisted Blockchain-IoMT Healthcare Framework with Role-Based Access Control for Critically Ill Patients

Fog-Assisted Blockchain-IoMT Healthcare Framework with Role-Based Access Control for Critically Ill Patients

Implementation of Attribute-Base Control in Personal Data Protection

This research addresses the growing concern of cybersecurity and access control in Python applications, providing actionable recommendations for improving Attribute-Based Access Control (ABAC) systems to better protect personal data. The study aims to evaluate ABAC's efficacy in managing access control within Python applications, particularly focusing on its ability to provide precise and fine-grained control over personal data access. By analyzing three key attributes—user roles, data classification, and access times—within Python applications, the research methodically assesses ABAC's performance and challenges in implementation. The findings, with a significant proportion of 70%, underscore ABAC's advantages over traditional models like Discretionary Access Control (DAC) and Role-Based Access Control (RBAC), emphasizing its capability to provide precise and fine-grained control over personal data access. Additionally, the research identifies and addresses three main challenges in ABAC implementation: attribute management complexity 15%, the necessity for standardization 10%, and interoperability issues 5%. This research has far-reaching implications, highlighting the importance of meticulous planning and modeling for successful ABAC deployment. By enriching our understanding of ABAC in Python-based environments, the study offers insights for enhancing cybersecurity measures and access control strategies in personal data protection.

An Advance Learning Platform for Slow Learners

LearnIt is an innovative educational system crafted to identify and support slow learners through advanced technological solutions. It employs the MERN stack (MongoDB, Express.js, React.js, Node.js) to offer a personalized learning environment tailored to meet individual student needs. This system not only enhances the educational experience for students but also provides tools to facilitate effective communication between teachers and parents, ensuring a cohesive support network for the learner. Additionally, LearnIt empowers educators with data-driven insights, allowing for informed decision-making and targeted interventions. The traditional educational model often overlooks the unique needs of slow learners, resulting in disengagement and poor academic performance. LearnIt addresses this challenge by integrating comprehensive assessment tools and personalized learning strategies within its platform. By identifying slow learners early, the system provides timely interventions that can significantly improve their learning trajectory. The use of the MERN stack ensures that LearnIt is both scalable and responsive. The assessment and monitoring module is at the core of LearnIt’s functionality. The development of LearnIt involved a detailed analysis of the educational needs of slow learners, incorporating feedback from educators, parents, and the students themselves. This user-centered approach ensured that the system's design and functionality were closely aligned with real-world requirements. Key modules of LearnIt include user management, assessment and monitoring, and communication tool. The user management module handles registration, authentication, and role-based access control, ensuring secure and personalized experiences for all users. The assessment and monitoring module is at the core of LearnIt’s functionality. It includes tools for creating and administering assessments, tracking student performance, and generating detailed reports. This module helps in identifying slow learners by analyzing their performance data and providing actionable insights to educators. With these insights, teachers can develop customized learning plans that address the specific needs of each student, fostering an inclusive and effective educational environment. Communication is another critical component of LearnIt. The platform includes features that facilitate seamless communication between teachers, parents, and students. This ensures that parents are kept informed about their child's progress and can collaborate with teachers to provide consistent support. Effective communication helps in building a supportive learning ecosystem, which is essential for the success of slow learners. The implementation of LearnIt has shown promising results. Preliminary studies indicate that the system significantly improves the identification and support of slow learners. LearnIt represents a significant advancement in the field of educational technology. By focusing on the needs of slow learners and leveraging the capabilities of the MERN stack, it offers a comprehensive solution that enhances learning outcomes. The system's personalized approach, combined with robust data analytics and effective communication tools, ensures that every student receives the support they need to thrive. As we continue to refine and expand LearnIt, its impact on educational equity and student success will undoubtedly grow, offering new opportunities for learners who have been historically underserved by traditional educational models. Keywords: Slow learners, education, literacy, mental development, intelligence.