Access Control Research Articles

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

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

ZTA-IoT: A Novel Architecture for Zero-Trust in IoT Systems and an Ensuing Usage Control Model

Recently, several researchers motivated the need to integrate Zero Trust (ZT) principles when designing and implementing authentication and authorization systems for IoT. An integrated Zero Trust IoT system comprises the network infrastructure (physical and virtual) and operational policies in place for IoT as a product of a ZT architecture plan. This paper proposes a novel Zero Trust architecture for IoT systems called ZTA-IoT. Additionally, based on different types of interactions between various layers and components in this architecture, we present ZTA-IoT-ACF, an access control framework that recognizes different interactions that need to be controlled in IoT systems. Within this framework, the paper then refines its focus to object-level interactions, i.e., interactions where the target resource is a device (equivalently a thing) or an information file generated or stored by a device. Building on the recently proposed Zero Trust score-based authorization framework (ZT-SAF) we develop the object-level Zero Trust score-based authorization framework for IoT systems, denoted as ZTA-IoT-OL-SAF, to govern access requests in this context. With this machinery in place, we finally develop a novel usage control model for users-to-objects and devices-to-objects interactions, denoted as UCON IoT . We give formal definitions, illustrative use cases, and a proof-of-concept implementation of UCON IoT . This paper is a first step toward establishing a rigorous formally-defined score-based access control framework for Zero Trust IoT systems.

Business process discovery as a service with event log privacy and access control over discovered models

Business process discovery as a service with event log privacy and access control over discovered models

An efficient key tag missing identification protocol for multiple regions in the Industrial Internet of Things

Radio Frequency Identification (RFID) is a special wireless network technology that uses radio frequency communication to achieve non-contact automatic identification through tags affixed to items. As one of the core technologies for physical layer communication in the Industrial Internet of Things(IIoT), RFID technology has been widely used in inventory management, supply chain management, access control identification, and many other areas. In practical application scenarios, the value and importance of some special tags far exceeds that of other tags. Therefore, in many cases, managers only need to pay attention to the status of these special tags, i.e., key tags, and not the status of all tags. However, the response of ordinary tags to reader commands will seriously interfere with identifying key missing tags. Therefore, we propose a novel protocol, Random Grouped Missing Key Tag Identification (RGKMTI), designed for multi-region key tag identification. RGKMTI employs a region-based sequential tag identification method to identify each group of tags individually, which effectively reduces collision slots and utilizes the duality property of slots containing only two key tags during the missing key tag verification phase, where the reader can verify two tags by transmitting at most a single tag ID, which significantly reduces the transmission load and shortens the total execution time. The experimental results show that the RGKMTI is significantly better than the existing schemes in terms of execution time and time efficiency, even though it reduces the identification time by 19.83% and improves the time efficiency by 6.82% compared with the best existing schemes.

A Review of Blockchain-Based Research on E-Health Data Sharing

With the development of medical data informatization, the data security, data privacy, and data controllability of electronic medical data with patient's personal privacy have gained social attention. Blockchain technology has decentralized characteristics, and the development of blockchain technology provides an effective solution for the secure sharing of e-medical data. Many scholars have proposed e-medical data security sharing solutions based on blockchain technology, and these solutions have attempted to solve the security problems encountered in sharing traditional e-medical data. By summarizing and analyzing the schemes in this field, this study analyzes the advantages, disadvantages, and challenges that the current mainstream schemes have, mainly in terms of blockchain data security storage, access control security sharing, and so on, in order to promote the further development of the e-medical data security sharing scheme based on blockchain technology. This study analyzes the blockchain-based e-medical data security sharing scheme proposed by scholars by analyzing the blockchain technology, cryptography technology, smart contract technology, P2P network technology and other related topics. Through systematic analysis and comparison, it explores the challenges of the current research and provides references for the future proposals of blockchain technology-based schemes.

The Need for Adaptive Access Control System at the Network Edge

The emergence of edge computing, characterized by its distributed nature and real-time processing, necessitates a paradigm shift in access control mechanisms. Traditional, static methods struggle to adapt to the dynamic and heterogeneous environment of edge computing. This research addresses this gap by proposing an Adaptive Risk-Based Access Control (ARBAC) model specifically designed for edge environments. The objective of this research is to develop a robust access control system that dynamically responds to the changing security landscape of edge computing. The proposed ARBAC model integrates real-time data on user context, resource sensitivity, action severity, and risk history to dynamically assess the security risk associated with each access request. This approach ensures a balance between robust security and user experience by tailoring access controls based on the specific context. The research builds upon the growing recognition of the limitations of traditional access control methods in edge environments. Existing literature highlights the need for adaptive and risk-based access control models to address the dynamic nature of edge computing. This research contributes to this evolving field by proposing an ARBAC model that leverages real-time information for contextually relevant access decisions. The proposed ARBAC model offers several advantages. By dynamically adjusting access controls based on risk levels, the model enhances security and ensures compliance with regulatory requirements. Additionally, it improves network performance by reducing load and facilitating faster access to resources. Furthermore, the model's scalability makes it suitable for managing access in large-scale edge deployments. In conclusion, this research proposes an ARBAC model that aligns with the dynamic nature of edge computing environments. By leveraging real-time data and contextual information, the model offers a robust and adaptable approach to access control, promoting security, compliance, performance, and scalability in edge computing. This research paves the way for further exploration and implementation of ARBAC systems, empowering organizations to effectively manage access control in the evolving landscape of edge computing and IoT.

Analysis and Research on Secure Access Control Technology of Industrial Internet of Things Based on ZTM Model

The security threat of IIoT is becoming increasingly serious. In order to address this challenge, security access control technology based on the ZTM model has become a hot research topic. The aim of this study is to conduct in-depth analysis and research on the security access control technology applied by the ZTM model in industrial Internet of Things environments. By analyzing the current challenges of IIOT security and the limitations of traditional security models, this paper proposes a series of security access control technologies related to the ZTM model, aiming to quantify and evaluate the effectiveness of access control policies, zero trust of the system, and comprehensive risk assessment. By using empirical research methods, this study verified the feasibility of the proposed technology in actual industrial Internet of Things environments and demonstrated the significant effect of the ZTM model in reducing security risks and improving system credibility. The experimental results showed that the optimized security access control technology improved security performance by 28% and the missed detection rate was as low as 3.2%. This study provides useful insights for practical applications in the field of secure access control and provides a solid foundation for future research.

A blockchain-based engineering design review service trading scheme for digital building permits

Engineering design review, which is a time-consuming task in the building permit process, faces challenges such as a scarcity of design review specialists, vulnerability to manipulation and corruption, and a lack of traceability and transparency. This paper explores the integration of blockchain and online service trading in addressing these challenges. This paper proposes a platform-based design review service trading scheme, engaging a distributed network of experts to overcome resource limitations and integrates blockchain technology to improve process traceability. The system model is presented, an access control model and a design version management method are proposed. A prototype system is developed, and a real-world project is used to illustrate the online design review process. Security, function, and performance evaluations show that the scheme is secure and efficient. This study highlights blockchain's potential in digital building permits. Future work can explore advanced service matching algorithms in online design review service trading platforms.

Lightweight Image Encryption Using a Novel Chaotic Technique for the Safe Internet of Things

Recently, the field of lightweight cryptography (LWC) has emerged in response to the security needs of low-cost, widely used technology. It is essential to implement an encryption approach with access control to give less complex, more flexible, and safe access to sensitive data. In this work, a novel lightweight chaotic encryption approach with fuzzy access control is presented to encrypt light images in the IoT domain, while maintaining image quality. With the aid of multiplexer modeling and information shift register technology, the algorithm’s design combines random and chaotic mapping approach based on a specific password key with a predetermined number of fuzzy logic shifts on the password key for the image pixels. Further, to extract the private key with complexity and boost defense against attacks, a shift register and logical xor combination is employed. The simulation of the proposed model for AVR microcontroller has been done under MATLAB software and the design of various encryption components has been used to implement lightweight mapping. The proposed system has been evaluated in terms of histogram analysis, adjacent pixel correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, error mean square analysis, information entropy, pixel number change rate, integrated mean change intensity, peak signal-to-noise ratio, and time complexity. Remarkably, the proposed technique has demonstrated high efficiency. The simulation results show that the homogeneity, energy, contrast, NPCR, and UACI criteria have improved by 11.5%, 13.1%, 19%, 0.53%, and 0.12%, respectively, compared to other methods in other articles.

A Hybrid Anti-Collision Protocol Based on Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA) for Radio Frequency Identification (RFID) Readers

Radio Frequency Identification (RFID) technology plays a crucial role in various Internet of Things (IoT) applications, necessitating the integration of RFID systems into dense networks. However, the presence of numerous readers leads to collisions, degrading communication between readers and tags and compromising system performance. To tackle this challenge, researchers have proposed Medium Access Control (MAC) layer protocols employing different channel access methods. In this paper, we present a novel solution, the Distributed Time Slot Anti-Collision protocol (DTS-AC), which employs a new TDMA notification system to address Reader-to-Reader Interference (RRI), while incorporating FDMA-based frequency resource management to resolve Reader-to-Tag Interference (RTI) collision issues. Simulation results demonstrate that DTS-AC significantly improves performance in dense RFID networks by enhancing read rates, with scalability benefits based on the number of readers, channels, and Time Slots (TSs). Moreover, the cost-effectiveness of DTS-AC facilitates efficient deployment in RFID networks, emphasizing considerations of time delay and data sensitivity.

Application of Artificial Intelligence Technology in Vulnerability Analysis of Intelligent Ship Network

The improvement in transportation efficiency, security, safety, and environmental effects may be possible due to the impending advent of autonomous ships. Automatic situational awareness, risk detection, and intelligent decision-making are the key features of the intelligent ship network, differentiating it from conventional ships. There is an immediate need to implement a system for marine information management and network security due to the growing importance of this field, which poses a risk to national and societal stability due to factors, such as the diversity and complexity of marine information types, the challenges associated with data collection, and other similar factors. By recognizing different vulnerabilities and through research cases of the ship systems and Artificial Intelligence (AI) technologies, this paper presents Adaptive Fuzzy Logic-assisted Vulnerability Analysis of Intelligent Ship Networks (AFL-VA-ISN) in various cyberattack scenarios for autonomous ship intrusion detection and information management. Fuzzy logic has been combined with AI, providing a framework for handling uncertainty and imprecision in intelligent ship networks and effective decision-making. This work presents a method for detecting anomalies in risk data based on the collaborative control structure of the Ship Information System. Maintaining the network security of intelligent ships is the primary focus of this research, which mainly employed multi-sensor nodes to evaluate data containing information about malicious attacks and placed self-execution protection organize generating nodes into place to intercept and protect against attacks. The experimental outcomes demonstrate that the suggested AFL-VA-ISN model increases the data transmission rate by 99.2%, attack detection rate by 98.5%, risk assessment rate by 97.5%, and access control rate of 96.3%, and reduces the network latency rate of 11.4% compared to other existing models.

Integrated Cloud Security Framework for Confidentiality Integrity Availability with Multi Access Control

Integrated Cloud Security Framework for Confidentiality Integrity Availability with Multi Access Control

Continuous Integration for New Service Deployment and Service Validation Script for Vault

Modern cloud-native applications demand robust security measures to safeguard sensitive data such as passwords, API keys, and encryption keys. Managing these secrets securely within Kubernetes clusters presents a significant challenge. In response, this project proposes a comprehensive solution leverag- ing HashiCorp Vault, Kubernetes, and Docker to enhance secret management and strengthen overall security posture.HashiCorp Vault serves as a centralized secrets management tool, provid- ing encryption, access control, and auditing functionalities. By integrating Vault with Kubernetes, secrets can be dynamically generated, securely stored, and automatically injected into ap- plication pods at runtime. This approach reduces the exposure of sensitive information within containerized environments and mitigates the risk of unauthorized access. The project architecture involves deploying Vault within the Kubernetes cluster, utilizing Docker containers for seamless encapsulation and portability. Kubernetes’ native integrations with Vault, such as the Kubernetes Auth method and the Vault Agent Injector, streamline the authentication and authorization processes, ensuring secure communication between applications and Vault. The project involves deploying Vault in Kubernetes for secrets management, ensuring High Availability. It focuses on generating, storing, and managing secrets securely, leveraging Vault’s dynamic secrets engine for automatic rotation. Integration with Kubernetes employs authentication methods like Service Accounts and RBAC for granular access control. Dockerization ensures application consistency and portability, with Vault Agent containers enabling seamless secret injection. Security best practices, including least privilege access and encryption, are prioritized, along with regular auditing and monitoring. Overall, the project aims to establish a robust secrets management solution within Kubernetes while empha- sizing resilience, security, and compliance in handling sensitive information. Index Terms—Docker, DevOps, CI/CD, Automation, Secrets, Kubernetes, Vault, Security

Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning

Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multiaccess MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scalability. The results demonstrate that the DRL-based approach effectively balances the tradeoffs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks.

Three-phase coded triboelectric nanogenerator disk: A novel approach for generating diverse control instructions in human–machine interactions

Triboelectric nanogenerator (TENG) has been widely utilized in a variety of real-world applications such as the Internet of Things (IoT) and human–machine interactions (HMIs). However, current technologies are difficult to be implemented in the situations that require a large amount of control instructions, due to the limitation on the size and the cost of the TENG-based interface. In this paper, a sliding operation three-phase coded TENG based control disk HMI interface is developed by integrating three coded copper electrodes, polytetrafluoroethylene (PTFE) film, and electronic signal processing circuits. The three-phase coded TENG electrodes enable the ability to overcome the challenge of increasing the number of generatable control instructions. The proposed system successfully achieves thirty sensing transitions through the utilization of the three-phase patterned electrodes. To evaluate the performance of this system, four applications, including English letters typing, game control, smart home control, and authentication access control, are implemented in real-time. An embedded microcontroller with a Bluetooth interface is employed to process and transmit the triboelectric control signal. The proposed TENG control disk effectively supports all functionalities required for these applications. Overall, the successful implementation and support of these applications demonstrate the effectiveness and versatility of the proposed TENG control disk in real-world scenarios and potential environments.

Smart IV Bag and Health Monitoring System for Hospitals and Remote Places Using IOT

The health monitoring system utilizing an ESP32 controller and Arduino Nano presents an innovative approach to enhance patient care through continuous real-time monitoring of vital health parameters. This system integrates various sensors, including ECG, DHT11, HX711, and an R305 fingerprint sensor, to collect comprehensive physiological data and ensure secure access. The ESP32 microcontroller processes this data, providing immediate local alerts via a buzzer and LED indicators, while the Arduino Nano manages the GSM module to send remote notifications to healthcare professionals. Furthermore, the system employs IoT capabilities to transmit data to cloud platforms, facilitating remote monitoring and analysis. By automating the monitoring process, this project reduces the workload on healthcare staff and ensures timely medical interventions, thereby improving patient outcomes. The system's ability to provide real-time data visualization, secure access control, and remote monitoring aligns with the growing need for connected and intelligent healthcare solutions. This comprehensive health monitoring system not only enhances patient safety and care efficiency but also supports the advancement of modern healthcare infrastructures through robust data management and communication technologies.

Spatial design guidelines for waterfall tourist attraction: A case study in Wang Hin Lat Waterfall, Trang province

This article investigates the sustainable development of the Wang Hin Lat waterfall tourist attraction, focusing on spatial design and land use. A field survey collected physical data, including topography, tourist attraction resources, facilities, land use, and access while community needs were gathered through focus group meetings, and tourist behavior was observed. The analyzed data led to proposed guidelines for tourist attraction development, emphasizing area utilization, carrying capacity, layout plan, and management. Despite the waterfall's natural beauty and provincial popularity, deficiencies in infrastructure, utilities, facilities, and land use planning contribute to congestion issues. It is a small waterfall capable of accommodating a limited number of tourists. Recommendations for Wang Hin Lat waterfall development include clearly defining and prioritizing zoning for natural areas, recreation zones, service and office areas, shops, parking, and entrances. A suggested tourist limit of 300, with access control points, aims to manage congestion. Diversifying activities, such as promoting nature, culture, and lifestyle learning, developing nature trails, and establishing clear regulations, is essential for the sustainable coexistence of tourist attraction development and nature conservation.

A Comprehensive Review on Cybersecurity Issues and Their Mitigation Measures in FinTech

The fourth industrial revolution has seen the evolution and wide adoption of game-changing and disruptive innovation, "financial technologies (FinTech), around the globe. However, the security of FinTech systems and networks remains critical. This research paper comprehensively reviews cybersecurity issues and their mitigation measures in FinTech. Four independent researchers reviewed relevant literature from IEEE Xplore, ScienceDirect, Taylor & Francis, Emerald Insight, Springer, SAGE, WILEY, Hindawi, MDPI, ACM, and Google Scholar. The key findings of the analysis identified privacy issues, data breaches, malware attacks, hacking, insider threats, identity theft, social engineering attacks, distributed denial-of-service attacks, cryptojacking, supply chain attacks, advanced persistent threats, zero-day attacks, salami attacks, man-in-the-middle attacks, SQL injection, and brute-force attacks as some of the significant cybersecurity issues experienced by the FinTech industry. The review paper also suggested authentication and access control mechanisms, cryptography, regulatory compliance, intrusion detection and prevention systems, regular data backup, basic security training, big data analytics, use of artificial intelligence and machine learning, FinTech regulatory sandboxes, cloud computing technologies, blockchain technologies, and fraud detection and prevention systems as mitigation measures for cybersecurity issues. However, tackling cybersecurity issues will be paramount if FinTech is to realize its full potential. Ultimately, this research will help develop robust security mechanisms for FinTech systems and networks to achieve sustainable financial inclusion.

Coupling bit and modular arithmetic for efficient general-purpose fully homomorphic encryption

Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data. This property is desirable for outsourced computation of sensitive data as it relies solely on the underlying security of the cryptosystem and not in access control policies. Even though FHE is still significantly slower than unencrypted computation, practical times are possible for applications easily representable as low-order polynomials, since most FHE schemes support modular addition and multiplication over ciphertexts. If, however, an application cannot be expressed with low-order polynomials, then Boolean logic must be emulated. This bit-level arithmetic enables any computation to be performed homomorphically. Nevertheless, as it runs on top of the natively supported modular arithmetic, it has poor performance, which hinders its use in the majority of scenarios. In this work, we propose Bridging, a technique that allows conversion from bit-level to modular arithmetic and vice-versa. This enables the use of the comprehensive computation provided by bit-level arithmetic and the performance of modular arithmetic within the same application. Experimental results show that Bridging can lead to 1-2 orders of magnitude performance improvement for tested benchmarks and two real-world applications: URL denylisting and genotype imputation. Bridging performance comes from two factors: reduced number of operations and smaller multiplicative depth.