Access Control System Research Articles

Robust embedded access control system based on face and encrypted QR with RPi4

<p><span>Facial-based recognition systems are commonly used for building access control, with the accuracy and computing requirements still being improved. On the other hand, QR codes are gaining rising attention as an input interface to many embedded applications. This paper proposes an embedded access control system that customises both previous techniques to be implemented on the CPU of a low-cost Raspberry Pi 4 computer. The achieved system works smoothly with a frame rate of 8.27 FPS, increasing the accessing control's robustness compared to a system based on face recognition only. It also offers the ability to control the access of unknown faces. In tandem with integration, this strengthens security measures, improves user experience, and outperforms conventional access control approaches, creating an attractive offer for many businesses.</span></p>

An Efficient Multilayer approach for Securing E-Healthcare Data in Cloud using Crypto – Stego Technique

Healthcare data has been moving to cloud platforms in recent years, which has increased accessibility and scalability but also raised security issues. Ensuring data integrity and safeguarding private health information from unwanted access are critical. This paper presents a comprehensive strategy to integrate effective Elliptic Curve Cryptography ECC-AES with steganography techniques to improve the security of healthcare data in the cloud. ECCAES is especially well-suited for cloud situations with limited resources since it provides strong security with reduced key sizes. Confidentiality is guaranteed by encrypting healthcare data using ECC- AES before storage, reducing the possibility of data breaches. Steganography techniques are also integrated to improve security against skilled adversaries by adding an extra degree of obfuscation by concealing encrypted data inside innocuous files or images. Strict key management procedures, access control systems, and frequent security audits are important components of the proposed system that ensure adherence to Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) compliance requirements pertaining to healthcare data protection. Programs for employee awareness and training are also crucial for reducing the likelihood of human mistakes. Healthcare businesses can safely use cloud technology while protecting patient data integrity and privacy by putting in place multi-layered security safeguards. The proposed system provides the multilayer security on healthcare data in cloud environment than other existing systems.

Design and Development of Intelligent Access Control System Integrating Hi3861 and K210

Design and Development of Intelligent Access Control System Integrating Hi3861 and K210

Simulation-Based Evaluation of Advanced Threat Detection and Response in Financial Industry Networks Using Zero Trust and Blockchain Technology

The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.

A Dynamic Sandbox Detection Technique in a Private Cloud Environment

In specific private cloud scenarios, how to defend against malicious software and ensure data security is one of the current research hotspots, and sandbox is an important detection method. This paper proposes a dynamic behavior detection technique based on sandboxing, which real-time monitors and analyzes malicious software behavior. By improving the sandbox behavior weight, integrating virtual resources, and designing fine-grained access control, the detection accuracy and efficiency are enhanced based on zero trust access control system. The simulated attacks are identified on the testing platform, drawing knowledge graphs, achieving effective discovery and tracing. Meanwhile, this paper verified through experiments that the system consumption of the detection method is within an acceptable range, expanding the detection range and reducing the missed detection rate.

Vehicle And Driver Recognition for Access Control

The following paper proposes an advanced vehicle and driver recognition system to enhance security at vehicular entry-exit points in response to the growing needs of more efficient and secure access control systems at these checkpoints. The proposed system will integrate license plate recognition and facial recognition technologies using state of-the-art machine-learning models from Ultralytics: YOLOv8 and EasyOCR, respectively. It works in three stages: license plate detection, character alpha-numeric identification, and then driver identity with facial recognition. Hence, the two-tier authentication process based on license plate detection and facial identification ensures further prevention from unauthorized entry. Preliminary evaluation of the system also produced very high values for precision, recall, and the mean average precision, indicating that the VDR system offers substantial advances in effectiveness and security when compared to conventional methodologies for access control.

Cybersecurity in Healthcare: Securing Patient Health Information (PHI), HIPPA compliance framework and the responsibilities of healthcare providers.

Healthcare industry is major target for cyberattacks, making the protection of public health information (PHI) and Personal Identifiable Information (PII) a prime issue. In this digital era, with health organizations shifting to electronic health records and telemedicine, they are facing a major cybersecurity attack such as ransomware, phishing, and data breaches. These attacks compromise patient privacy, pose serious risks to patient safety, and hinder healthcare operations. The Health Insurance Portability and Accountability Act (HIPAA) provides a comprehensive compliance framework to protect PHI, wherein health providers shall undertake administrative, physical and technical safeguards. Despite these regulations, many healthcare providers struggle with achieving and maintaining HIPAA compliance due to limited resources, outdated technologies, and the rapidly evolving nature of cyber threats. This paper explores the HIPAA compliance framework, examining the specific responsibilities of healthcare providers to secure PHI. Key measures taken include periodic analysis of risks, establishment of encryption and access control systems, and comprehensive employee training to minimize risks of cyber-attacks. The study highlights that there is a growing need for healthcare providers to adopt proactive, adaptive cybersecurity strategies to deal with emerging threats. By following HIPAA regulations and updating security practices continuously, healthcare providers can protect the PHI, ensure regulatory compliance and safeguarding patient trust. The findings emphasize the role of adhering to regulation and innovation both in managing cyber risks for the healthcare sector.

Enhanced multimodal biometric access control system using chicken swarm optimization and self-organizing feature maps: a study on ear and iris recognition

Access control systems are crucial for securing sensitive data and system components by allowing authorized access while blocking unauthorized entities. Traditional unimodal biometric systems, make use of a single physiological or behavioral trait, have limitations such as susceptibility to spoofing and environmental constraints, leading to reduced reliability. This study explores an enhanced multimodal biometric access control system that combines ear and iris traits using an enhanced Self-Organizing Feature Map (SOFM) algorithm improved with Chicken Swarm Optimization (CSO). The system's performance is evaluated against traditional SOFM, with a focus on recognition accuracy and processing time. The data used to train the classifier for this study were collected from 190 individuals, encompassing a total of 2,280 images of iris, and ear traits. Preprocessing involved cropping, resizing, and grayscale conversion using histogram equalization. Feature extraction utilized Local Binary Patterns (LBP), followed by feature fusion at the feature level to create an integrated feature set. The enhanced SOFM algorithm was then applied for classification, with the CSO technique optimizing the learning rate and weight parameters for improved performance. At different thresholds, the CSO-SOFM classifier outperformed the standard SOFM classifier using metrics such as Sensitivity, Specificity, Precision, Accuracy and Recognition time.

Emergency Medical Access Control System Based on Public Blockchain

IT has made significant progress in various fields over the past few years, with many industries transitioning from paper-based to electronic media. However, sharing electronic medical records remains a long-term challenge, particularly when patients are in emergency situations, making it difficult to access and control their medical information. Previous studies have proposed permissioned blockchains with limited participants or mechanisms that allow emergency medical information sharing to pre-designated participants. However, permissioned blockchains require prior participation by medical institutions, and limiting sharing entities restricts the number of potential partners. This means that sharing medical information with local emergency doctors becomes impossible if a patient is unconscious and far away from home, such as when traveling abroad. To tackle this challenge, we propose an emergency access control system for a global electronic medical information system that can be shared using a public blockchain, allowing anyone to participate. Our proposed system assumes that the patient wears a pendant with tamper-proof and biometric authentication capabilities. In the event of unconsciousness, emergency doctors can perform biometrics on behalf of the patient, allowing the family doctor to share health records with the emergency doctor through a secure channel that uses the Diffie-Hellman (DH) key exchange protocol. The pendant’s biometric authentication function prevents unauthorized use if it is stolen, and we have tested the blockchain’s fee for using the public blockchain, demonstrating that the proposed system is practical.

Research on Network Security and Privacy Protection Technology of Autonomous Vehicle

With the rapid development of information technology and the increasing maturity of intelligent transportation systems, autonomous vehicles, as an important carrier of future travel, are gradually moving from concept to commercial application. However, the highly connected and intelligent nature of autonomous vehicles also makes them face unprecedented cybersecurity and privacy protection challenges. This paper deeply discusses the security threats and privacy disclosure risks of autonomous vehicles in network architecture, data transmission, data processing and other links, and systematically reviews the current technical means and research progress to address these challenges. This paper Outlines the data collection and transmission mechanism of autonomous vehicles, including the collection and processing of sensor data as well as the communication process between vehicles and the cloud, other vehicles and transportation infrastructure, and analyzes in detail the network security threats faced by autonomous vehicles, including remote control attacks, malware attacks, communication hijacking, etc. These threats can lead to serious consequences such as vehicle failure, data breach, or system crash. At the same time, the paper also points out the challenges faced by autonomous vehicles in terms of privacy protection, such as the risk of leakage of sensitive data such as personal location information and driving habits, as well as the risk of data abuse and sharing. In order to address the above challenges, this paper focuses on the technical research progress of cybersecurity and privacy protection of autonomous vehicles. In the aspect of network security, this paper discusses the key technical means such as data encryption, access control, intrusion detection and response system, and emphasizes the importance of security hardware and software in improving the overall security of the system. In terms of privacy protection, this paper proposes strategies such as anonymization and desensitization technology, privacy protection protocols and standards to protect users' privacy from invasion.

IoT Mechatronic Access Control System ePRO 1.4

IoT Mechatronic Access Control System ePRO 1.4

A Blockchain-Based Access Control System for Secure and Efficient Hazardous Material Supply Chains

A Blockchain-Based Access Control System for Secure and Efficient Hazardous Material Supply Chains

Student Apartment Access Control System Based on MTCNN-FaceNet Algorithm

Student Apartment Access Control System Based on MTCNN-FaceNet Algorithm

Towards accountable and privacy-preserving blockchain-based access control for data sharing

The integration of blockchain technology with Access Control (AC) systems presents novel opportunities for enhancing data security within decentralized architectures, which is drawing increasing attention in Data Sharing (DS) applications. However, existing works reveal a gap in achieving accountability for anonymous access in the absence of a centralized trusted authority. To address this issue, this paper introduces InvisiReveal, a novel Blockchain-Based AC (BBAC) framework that achieves permission invisibility, access anonymity, and accountability without extra trust assumptions. Users in InvisiReveal generate anonymous credentials to authenticate their requests using Zero Knowledge Proof. To enable accountability, a novel blockchain-oriented verifiable commitment (BC-VC) protocol is designed that allows a user to commit a confidential traceable tag to the blockchain. The system could unveil a malicious requester’s identity by opening the tag commitment under collaboration with the victim user and blockchain. We implement a prototype of InvisiReveal to evaluate its practicality, where an access request is verified within 5 ms.

Context-Aware Risk Attribute Access Control

Traditional access control systems exhibit limitations in providing flexible authorization and fine-grained access in the face of increasingly complex and dynamic access scenarios. This paper proposes a context-aware risk access control model to address these challenges. By developing a multi-level contextual risk indicator system, the model comprehensively considers real-time contextual information associated with access requests, dynamically evaluates the risk level of these requests, and compares the outcomes with predefined risk policies to facilitate access decisions. This approach enhances the dynamism and flexibility of access control. To improve the accuracy and reliability of risk assessments, we propose a combination weighting method grounded in game theory. This method reconciles subjective biases and the limitations of objective data by integrating both subjective and objective weighting techniques, thus optimizing the determination process for risk factor weights. Furthermore, smart contracts are introduced to monitor user behavior during access sessions, thereby preventing malicious attacks and the leakage of sensitive information. Finally, the model’s performance and authorization granularity are assessed through empirical experiments. The results indicate that the model effectively addresses the requirements of dynamic and fine-grained access scenarios, improving the system’s adaptability to risk fluctuations while safeguarding sensitive information.

Anti Face Spoofing System

Face spoofing, the act of using deceptive techniques such as printed photos or digital images to deceive facial recognition systems, poses a significant threat to the security of various applications, including biometric authentication and access control systems. This paper presents a concise yet effective approach to address the challenge of anti-face spoofing using Python within a limited codebase of 50 lines. The proposed solution leverages a combination of image processing techniques and machine learning algorithms to detect and prevent face spoofing attempts. A pre-trained deep neural network model for facial recognition is employed to extract essential facial features. Subsequently, the system utilizes image manipulation detection methods to identify anomalies indicative of face spoofing attacks. The implementation showcases the simplicity and efficiency of the proposed anti-face spoofing technique, demonstrating the potential for integration into real- world applications with minimal computational overhead. The concise Python code enables easy adoption and adaptation for developers aiming to enhance the security of facial recognition systems against face spoofing threats. The experimental results demonstrate the effectiveness of the approach in accurately distinguishing between genuine and spoofed facial images, thereby contributing to the robustness of facial recognition systems in the presence of adversarial attacks. Keywords - Liveness Detection, Presentation Attack Detection, Spoof Detection Algorithms, Biometric Security, Face Recognition Robustness, 3D Face Modeling, Texture Analysis, Deep Learning Anti- Spoofing, Multi-Spectral Imaging, Behavioral Biometrics, Challenge-Response Mechanisms, Optical Flow Analysis, Pulse Detection, Surface Reflectance, Depth Sensing, Motion Analysis, Pattern Recognition, Anti-Spoofing Networks, Temporal Feature Extraction, Anomaly Detection.

Deep Learning System for User Identification Using Sensors on Doorknobs.

Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring patterns of their interactions with a doorknob via an embedded accelerometer and gyroscope and by applying deep-learning-based algorithms to these measurements. Our identification results obtained from 47 users show an accuracy of 90.2%. When the sex of the user is used as an input feature, the accuracy is 89.8% in the case of male individuals and 97.0% in the case of female individuals. We study how the accuracy is affected by the sample duration, finding that is its possible to identify users using a sample of 0.5 s with an accuracy of 68.5%. Our results demonstrate the feasibility of using patterns of motor activity to provide access control, thus extending with it the set of alternatives to be considered for behavioral biometrics.

INTERNAL CONTROL ON MILITARY ASSETS ACCORDING TO MILK AL-DAULAH IN ISLAMIC ECONOMIC LAW

This article aims to examine the internal control system of national military assets in Indonesia at the Kuta Alam Banda Aceh barracks, reviewed according to the concept of milk al-daulah. The approach taken by the author is a juridical-empirical study. The research findings indicate that to address issues in the internal control system of assets in the Indonesian National Army (TNI) at the Kuta Alam Banda Aceh barracks, a holistic approach is required to effectively tackle existing challenges. It is crucial to update and refine the inventory process to ensure that asset data is more accurate and current. Implementing an integrated, technology-based asset management system will facilitate tracking and monitoring. Furthermore, asset maintenance should be scheduled regularly and its implementation should be ensured to prevent damage that could increase repair costs. Enhancements in surveillance technology, such as modern CCTV and access control systems, are essential to reduce the risk of theft and damage. Compliance with policies should be strengthened through intensive training and consistent enforcement to ensure procedures are properly followed. Risk management also needs improvement with more effective and proactive mitigation strategies. Additionally, the use of advanced technology can support efficiency in asset management and reporting. Transparency should be increased by providing clear and accessible reporting mechanisms, while budget management should be improved to ensure efficient allocation. Regular personnel training is important to keep their skills relevant, and routine audits and evaluations should be conducted to maintain the effectiveness of the internal control system.

Power Trading and Access Control Scheme Based on IPFS and Blockchain

In recent years, a large number of renewable energy power plants have been built all over the country, which has led to a sharp increase in the pressure on the current centralized electricity trading platform. There are data storage bottlenecks and data privacy problems in the current blockchain-based power transaction and access control system. In this paper, the interstellar file system is proposed to store the data of the new distributed power trading platform in a distributed way, so as to improve the storage capacity of the physical nodes; An improved ciphertext strategy attribute encryption scheme, which is lightweight, traceable and supports outsourced decryption combined with state secret algorithm, is adopted to integrate the user’s unique identity into the user’s private key and part of the expensive decryption calculation is outsourced to the cloud server to realize fine-grained data access control in the electricity market. The experimental results show that compared to the basic CP-ABE scheme, when the encryption attribute is 100, the decryption time of the proposed scheme is reduced from 556[Formula: see text]ms to 1.1[Formula: see text]ms; Compared to blockchain-based solutions alone, when storing data of 15[Formula: see text]MB, GAS consumption decreases from 2968738133 to 89360. The scheme can greatly improve the storage capacity of the system, improve the operational efficiency and meet the actual performance requirements.

Face Detection and Recognition for Criminal Identification System

This project showcases a powerful Face Detection and Recognition system intended to improve law enforcement skills by speeding up the identification of suspects in criminal cases. The system uses scalable databases and real-time computation, however instead of using traditional web sources for face detection. Accuracy and dependability are ensured by using the LBPH algorithm for face recognition and the Haar-cascade classifier for feature detection. The smtplib library allows the system to send a Gmail notification when it finds a matching face. The system that has been built contributes to public safety initiatives by improving law enforcement operations and demonstrating versatility for different face detection circumstances. The outcomes show how successfully the system can detect faces in real-time circumstances. The system achieves great performance by creating dynamic datasets and integrating algorithms. Integrating Gmail improves law enforcement response by facilitating prompt contact of identified suspects. This study highlights the role that surveillance technology plays in public safety and crime prevention, and it provides law enforcement authorities with useful solutions. Furthermore, the uses of this facial recognition and detection technology go beyond police enforcement. It can expedite attendance tracking and improve security in access control systems. Because of its adaptability, it can be used in a variety of industries and is expected to have a significant impact.