Access Control Applications Research Articles

Development of A Palm-Vein Recognition System for Identification and Verification Systems using Enhanced Convolutional Neural Network

Biometric authentication systems have gained significant attention in access control applications due to their ability to provide enhanced security and convenience. Among various biometric modalities, palm-vein recognition has emerged as a promising approach, offering high accuracy, reliability, and resistance to forgery. However, existing palm-vein recognition systems often face challenges in implementation costs, computational efficiency, and performance limitations. This research aimed to develop an enhanced palm-vein recognition system for access control applications by optimizing a Convolutional Neural Network (CNN) architecture. A palm-vein dataset comprising 1000 images from 200 LAUTECH students was acquired, with 5 images per individual. The dataset was split into 700 training images and 300 testing images. The acquired images were pre-processed for quality enhancement and region of interest extraction. A Gravitational Search Algorithm (GSA) optimized CNN (GSA-CNN) was then employed to extract deep features from the pre-processed images, which were classified using a SoftMax layer. Experimental results revealed that the CNN technique achieved a specificity, sensitivity, False Positive Rate (FPR), accuracy of 74.60%, 79.89%, 25.40%, 77.67% at 117.52 seconds, respectively. In contrast, the proposed GSA-CNN technique demonstrated superior performance, achieving a specificity, sensitivity, FPR, accuracy of 92.06%, 92.53%, 7.94%, 92.33% at 97.14 seconds, respectively. The GSA-CNN system outperformed the conventional CNN approach in terms of accuracy, specificity, sensitivity, FPR, and processing time, demonstrating its potential for reliable and efficient palm-vein recognition in access control applications. The findings have significant implications for developing robust and secure access control systems, contributing to enhanced privacy and security across various domains.

Speaker Recognition Using MFCC-BPNN-HHO

Speaker recognition plays a pivotal role in speech processing. This paper proposes an enhancement to the Backpropagation Neural Network (BPNN) by incorporating Harris Hawks Optimization (HHO) for weight optimization, and evaluates its performance compared to the standalone BPNN. Both methods employ Mel Frequency Cepstral Coefficients (MFCC) for feature extraction from input data. The study assesses the proposed system on a dataset comprising 10 speakers, with each providing 10 utterances. Results demonstrate that the integrated MFCC-BPNN-HHO approach outperforms the standalone BPNN, achieving enhanced accuracy in speaker recognition tasks. Specifically, the accuracy of the BPNN-HHO was found to be significantly higher than that of the BPNN alone, indicating the effectiveness of the HHO optimization technique in improving speaker recognition accuracy. This study underscores the potential of integrating optimization algorithms like HHO with BPNN to further refine speaker recognition systems and contribute to advancements in speech processing technology. This approach has promising applications in access control, identity verification and other security-related domains where biometric authentication is essential. Keywords—MFCC, BPNN, HHO

ABAC as Access Control Solution for Digital Evidence Storage

Digital evidence is content that must be protected against access and use by parties who should not have the authority to do so. Some protection parameters for access to digital evidence must be implemented to ensure its integrity and authenticity. Access to digital evidence is not enough to be facilitated only with authorization and authentication mechanisms but must also be facilitated with other aspects of access by users according to their level of authority. One approach is to use the concept of access control. The study of access control to digital evidence is essential. However, studies on this matter are still limited. Among the many access control models, the application of access control based on attribute variations is a concept that can be applied to the context of access to digital evidence. This paper discusses policy design and modeling using attribute-based access control (ABAC) with four attributes: subject, resource, action, and environment. Then, it implements and tests various requests to the system based on attribute variations and possible algorithms. This study supports the security of digital evidence storage systems through access control to the resources it manages using the Policy Statement, Policy Modeling, Policy Implementation, and Policy Validation approaches. The application of the access control design shows that the ABAC concept has been successfully applied as an access control solution for digital evidence stored in digital evidence storage systems. The built policy design was successfully validated using ACPT Tools, concluding that there was no inconsistency or incompleteness.

Multimodal Biometric Login System Using Face and Signature

Biometrics has developed to be one of the most relevant technologies used in Information Technology (IT) security. Uni-modal biometric systems have a variety of problems which decreases the performance and accuracy of these systems. One way to overcome the limitations of the Unimodal biometric systems is through fusion to form a multimodal biometric system. Hence this process is developed based on Multimodal Biometric system based on Face, Finger print, Signature etc. Multimodal biometric system employing Convolutional Neural Networks (CNNs) for effective feature extraction and classification. The system combines facial and signature biometrics, harnessing the unique advantages of each modality to create a more resilient and accurate authentication framework. Multimodal biometric system with CNN integration holds promise for applications in secure access control, financial transactions, and other domains where reliable authentication is crucial. Its adaptability and scalability make it a viable solution for addressing the evolving challenges in biometric security systems. Key Word: Unimodal biometric system , Multimodal Biometric system , Convolutional Neural Networks (CNNs),deep learning.

Black hole algorithm along edge detector and circular hough transform based iris projection with biometric identification systems

The circular parameters between the pupil and the iris are found using current iris identification techniques but the accuracy creates an issue for the detection process during image processing. The procedure of extracting the iris region from an eye image using circular parameters can be improved via approximately too many approaches in literature but remain some portions under slightly unconstrained circumstances. In this study, we presented a Black Hole Algorithm (BHA) along the Canny edge detector and circular Hough transform-based optimization technique for circular parameter identification of iris segmentation. The iris boundary is discovered using the suggested segmentation approach and a computational model of the pixel value. The BHA looks for the central radius of the iris and pupil. The system uses MATLAB to test the CASIA-V3 database. The segmented images exhibit 98.71% accuracy. For all future access control applications, the segmentation-based BHA is effective at identifying the iris. The integration of the BHA with the Hough transforms and Canny edge detector is the main method by which the iris segmentation is accomplished. This novel technique improves the accuracy and effectiveness of iris segmentation, with potential uses in image analysis and biometric identification.

Revolutionizing Home Security: A Comprehensive Overview of an Advanced RFID Door Lock System for Keyless Access and Smart Home Protection

The abstract presents a cutting-edge RFID door lock system that does away with traditional physical keys to improve home security. The system uses state-of-the-art components, such as an RFID (Radio-Frequency Identification) reader and an Arduino microcontroller, to provide a safe, keyless access option for homes. This technology provides homes with more protection and convenience, which is a comfort in a world where theft and unwanted access are ongoing issues. Conventional keys are not the best option for home security because they can be stolen, lost, or duplicated. By using customized RFID cards or key fobs, the RFID door lock system, on the other hand, streamlines the entry procedure while significantly lowering the possibility of unwanted access. An RFID reader module, an Arduino microcontroller, and an electronic locking mechanism are the three most crucial parts of the system. Once the authenticity of the card has been confirmed by the RFID reader, the Arduino microcontroller will utilize the obtained data to initiate the lock. Additionally, the system can be improved with features like smartphone applications for remote access control, which let homeowners monitor and grant access from a distance. This RFID door lock system has many advantages for homeowners, including strong security features, user-friendliness, and the capacity to monitor access events. These features provide homeowners with convenience and peace of mind. RFID-based security systems are an important development in the rapidly developing field of smart home technology.

Non-Fungible Token Integration for Secure Access Control, Monetization, and Real-world Applications

Non-Fungible Token Integration for Secure Access Control, Monetization, and Real-world Applications

Design of a Mobile Access Control Application to Optimize Security and Efficiency in the Entry and Exit of University Students in Peru

The main objective of this research work is to design a mobile access control application to optimize security and efficiency in the entry and exit of university students in Peru. For the development of this work, the RUP methodology was used and among its different stages the purpose of the work and the way in which this application was designed are explained. Also, we know that the technological advance with the passing of them is increasing is beneficial to use for problems that may arise in society, education, etc. in this case for the safety and efficiency of entry and exit of university students. Finally, a well-designed and properly implemented mobile application can be a powerful tool to optimize security and efficiency in the access of university students in Peru, thus contributing to a more effective and convenient educational experience for all involved.

A Survey on Embedding Iris Biometric Watermarking for User Authentication

This paper proposes an innovative approach called "Embedding Iris Biometric Watermarking" for user authentication. By utilizing the unique characteristics of the iris, a secure watermark is generated and embedded into the biometric data. This technique enhances the security and robustness of authentication systems, offering advantages such as high security, resistance to attacks, and non-intrusiveness. The proposed method has potential applications in access control, secure transactions, and digital rights management, providing a reliable solution for ensuring the integrity and confidentiality of digital systems and services.

Preference-Aware User Access Control Policy in Internet of Things.

There are multiple types of services in the Internet of Things, and existing access control methods do not consider situations wherein the same types of services have multiple access options. In order to ensure the QoS quality of user access and realize the reasonable utilization of Internet of Things network resources, it is necessary to consider the characteristics of different services to design applicable access control strategies. In this paper, a preference-aware user access control strategy in slices is proposed, which can increase the number of users in the system while balancing slice resource utilization. First, we establish the user QoS model and slice QoS index range according to the delay, rate and reliability requirements, and we select users with multiple access options. Secondly, a user preference matrix is established according to the user QoS requirements and the slice QoS index range. Finally, a preference matrix of the slice is built according to the optimization objective, and access control decisions are made for users through the resource utilization state of the slice and the preference matrix. The verification results show that the proposed strategy not only balances slice resource utilization but also increases the number of users who can access the system.

Automatic speaker verification system for dysarthric speakers using prosodic features and out-of-domain data augmentation

A communication disorder is an impairment of a person’s ability to talk or communicate appropriately. Dysarthria is a common neuro-motor speech communication disorder that can be caused by neurological damage. Dysarthria may affect the articulation, phonation, and prosody of a speaker. Dysarthria patients have poor neuromotor coordination and other physical impairments, making it difficult to utilize an interactive keyboard or other user interfaces. The ASV system can make biometric applications more accessible to dysarthric speakers by eliminating the need for them to remember cumbersome and unique authentication numbers and passwords. In this paper, we presented a study on developing an automatic speaker verification (ASV) system for dysarthria patients with varying speech intelligibility to assist them in remote access control and voice-based biometric applications. In the initial part of our proposed approach, we included a duration modification-based data augmentation module in the front end of the ASV system. Since prosody deficits are one of the early indicators of dysarthria, we investigated the role of prosodic variables in combination with the traditional Mel-frequency cepstral coefficients (MFCC). The prosodic variables explored in this study include pitch, loudness, and voicing probability. Separate i-vector and x-vector models are trained and compared using individual MFCC, prosodic variables, and their combinations. The experimental results showed that the proposed approach based on combining MFCC and prosody features along with duration-modification-based data augmentation produced promising results.

Designing a Private and Secure Personal Health Records Access Management System: A Solution Based on IOTA Distributed Ledger Technology.

The privacy and security of patients' health records have been an ongoing issue, and researchers are in a race against technology to design a system that can help stop the compromising of patient data. Many researchers have proposed solutions; however, most solutions have not incorporated potential parameters that can ensure private and secure personal health records management, which is the focus of this study. To design and develop a solution, this research thoroughly investigated existing solutions and identified potential key contexts. These include IOTA Tangle, Distributed Ledger Technology (DLT), IPFS protocols, Application Programming Interface (API), Proxy Re-encryption (PRE), and access control, which are analysed and integrated to secure patient medical records, and Internet of Things (IoT) medical devices, to develop a patient-based access management system that gives patients full control of their health records. This research developed four prototype applications to demonstrate the proposed solution: the web appointment application, the patient application, the doctor application, and the remote medical IoT device application. The results indicate that the proposed framework can improve healthcare services by providing immutable, secure, scalable, trusted, self-managed, and traceable patient health records while giving patients full control of their own medical records.

Rippling Colloidal Polyelectrolyte Complex for Customized Fingerprints with High Tactile Perception.

Fingerprints possess wide applications in personal identification, tactile perception, access control, and anti-counterfeiting. However, latent fingerprints are usually left on touched surfaces, leading to the leakage of personal information. Furthermore, tactile perception greatly decreases when fingerprints are covered by gloves. Customized fingerprints are developed to solve these issues, but it is a challenge to develop fingerprints with various customized patterns using traditional techniques due to their requiring special templates, materials, or instruments. Inspired by ripples on the lake, blowing air is used to generate surface waves on a colloidal polyelectrolyte complex, leading to vertical stratification and the accumulation of particles near the top of the film layer. As water rapidly evaporates, the viscosity of these particles significantly increases and the wave is solidified, forming fingerprint patterns. These customized fingerprints integrate functions of grasping objects, personal identification without leaving latent fingerprints and tactile perception enhancement, which can be applied in information security, anti-counterfeiting, tactile sensors, and biological engineering.

Widening Blockchain Technology toward Access Control for Service Provisioning in Cellular Networks.

The attention on blockchain technology (BCT) to create new forms of relational reliance has seen an explosion of new applications and initiatives, to assure decentralized security and trust. Its potential as a game-changing technology relates to how data gets distributed and replicated over several organizations and countries. This paper provides an introduction to BCT, as well as a review of its technological aspects. A concrete application of outsource access control and pricing procedures in cellular networks, based on a decentralized access control-as-a-service solution for private cellular networks, is also presented. The application can be used by service and content providers, to provide new business models. The proposed method removes the single point of failure from conventional centralized access control systems, increasing scalability while decreasing operational complexity, regarding access control and pricing procedures. Design and implementation details of the new method in a real-world scenario using a private cellular network and a BCT system that enables smart contracts are also provided.

Toward Designing a Secure Authentication Protocol for IoT Environments

Authentication protocol is a critical part of any application to manage the access control in many applications. A former research recently proposed a lightweight authentication scheme to transmit data in an IoT subsystem securely. Although the designers presented the first security analysis of the proposed protocol, that protocol has not been independently analyzed by third-party researchers, to the best of our knowledge. On the other hand, it is generally agreed that no cryptosystem should be used in a practical application unless its security has been verified through security analysis by third parties extensively, which is addressed in this paper. Although it is an efficient protocol by design compared to other related schemes, our security analysis identifies the non-ideal properties of this protocol. More specifically, we show that this protocol does not provide perfect forward secrecy. In addition, we show that it is vulnerable to an insider attacker, and an active insider adversary can successfully recover the shared keys between the protocol’s entities. In addition, such an adversary can impersonate the remote server to the user and vice versa. Next, the adversary can trace the target user using the extracted information. Finally, we redesign the protocol such that the enhanced protocol can withstand all the aforementioned attacks. The overhead of the proposed protocol compared to its predecessor is only 15.5% in terms of computational cost.

Quantum face recognition protocol with ghost imaging

Face recognition is one of the most ubiquitous examples of pattern recognition in machine learning, with numerous applications in security, access control, and law enforcement, among many others. Pattern recognition with classical algorithms requires significant computational resources, especially when dealing with high-resolution images in an extensive database. Quantum algorithms have been shown to improve the efficiency and speed of many computational tasks, and as such, they could also potentially improve the complexity of the face recognition process. Here, we propose a quantum machine learning algorithm for pattern recognition based on quantum principal component analysis, and quantum independent component analysis. A novel quantum algorithm for finding dissimilarity in the faces based on the computation of trace and determinant of a matrix (image) is also proposed. The overall complexity of our pattern recognition algorithm is O(N,log N)—N is the image dimension. As an input to these pattern recognition algorithms, we consider experimental images obtained from quantum imaging techniques with correlated photons, e.g. “interaction-free” imaging or “ghost” imaging. Interfacing these imaging techniques with our quantum pattern recognition processor provides input images that possess a better signal-to-noise ratio, lower exposures, and higher resolution, thus speeding up the machine learning process further. Our fully quantum pattern recognition system with quantum algorithm and quantum inputs promises a much-improved image acquisition and identification system with potential applications extending beyond face recognition, e.g., in medical imaging for diagnosing sensitive tissues or biology for protein identification.

Access Control Application Prevention and Mitigation of Cyber Attacks

In the rapidly evolving landscape of cyber threats, safeguarding digital assets has become paramount for organizations worldwide. This paper presents a comprehensive analysis of access control applications as a frontline defense against cyber intrusions. We delve into the mechanics of contemporary access control systems, highlighting their role in preventing unauthorized access and ensuring data integrity. Drawing on real-world case studies, we demonstrate the efficacy of robust access control measures in thwarting cyber-attacks and mitigating their potential impacts. Further, we explore the challenges faced in implementing these systems, such as ensuring scalability, managing user privileges, and maintaining system resilience. Our findings underscore the indispensable nature of access control applications in today’s digital age. By offering actionable insights and best practices, this paper aims to guide organizations in fortifying their digital infrastructures against the ever-present threat of cyberattacks. To ensure the security of data, it’s essential to update automation processes periodically. This not only safeguards the transfer of information, ensuring its authenticity and confidentiality, but also prevents tampering or misinformation. Incorporating access control into the security strategy can address this concern. Access control restricts data access to designated individuals within the organization. However, there’s an inherent risk: permissions could inadvertently be granted to unintended parties. To mitigate this, we recommend several solutions, with multifactor authentication being a prominent option for future implementation.

A Novel Length-Flexible Lightweight Cancelable Fingerprint Template for Privacy-Preserving Authentication Systems in Resource-Constrained IoT Applications

Fingerprint authentication techniques have been employed in various Internet of Things (IoT) applications for access control to protect private data, but raw fingerprint template leakage in unprotected IoT applications may render the authentication system insecure. Cancelable fingerprint templates can effectively prevent privacy breaches and provide strong protection to the original templates. However, to suit resource-constrained IoT devices, oversimplified templates would compromise authentication performance significantly. In addition, the length of existing cancelable fingerprint templates is usually fixed, making them difficult to be deployed in various memory-limited IoT devices. To address these issues, we propose a novel length-flexible lightweight cancelable fingerprint template for privacy-preserving authentication systems in various resource-constrained IoT applications. The proposed cancelable template design primarily consists of two components: 1) length-flexible partial-cancelable feature generation based on the designed reindexing scheme and 2) lightweight cancelable feature generation based on the designed encoding nested difference XOR scheme. Comprehensive experimental results on public databases FVC2002 DB1–DB4 and FVC2004 DB1–DB4 demonstrate that the proposed cancelable fingerprint template achieves equivalent authentication performance to state-of-the-art methods in IoT environments, but our design substantially reduces template storage space and computational cost. More importantly, the proposed length-flexible lightweight cancelable template is suitable for a variety of commercial smart cards (e.g., C5-M.O.S.T. Card Contact Microprocessor Smart Cards CLXSU064KC5). To the best of our knowledge, the proposed method is the first length-flexible lightweight, high-performing cancelable fingerprint template design for resource-constrained IoT applications.

Decentralized Identity Management Using Blockchain

This article explores the usage of decentralised identity (DID) management using blockchain in global organisations to support secure usage of information resources. Blockchain as technology was initially introduced as a cryptocurrency and there have been challenges in its adoption for enterprise applications such as identity management. DID is emerging as one of the strong blockchain adoption use cases. Industry pioneers and users across domains have started exploring DID use cases, which help better protect their personal data and application access control as compared to traditional, central, or federated identity management models. In this exploratory work, the authors employ qualitative secondary case-based study research methodology to understand the challenges of the current digital identity management landscape and explore the possible benefits of DID as an emerging identity management paradigm. They propose a conceptual cube framework for analysing and studying various DID platforms thereby contributing to both the theory and practice of digitally secure identity.

Optimized scheduling method in 6TSCH wireless networks

IEEE802.15.4e-TSCH is a mode exploited by the Internet of Things. Time Slotted Channel Hopping (TSCH) presents an upgrade to the IEEE 802.15.4 to build a Medium Access Control (MAC) for low power and loss network applications in IoT. This norm defines the concept of TSCH based on channel hopping and reservation of bandwidth to achieve energy efficiency, as well as consistent transmissions. Centralized approaches have been proposed for planning TSCH. They have succeeded in increasing network efficiency and reducing latency, but the scheduling length remains not reduced. However, distributed solutions appear to be more stable in the face of change, without creating a priori assumptions about the topology of the network or the amount of traffic to be transmitted. A distributed scheduling allowing neighboring nodes to decide on a coordination system operated by a minimal scheduling feature is currently proposed by the 6TiSCH working group. This scheduling allows sensor nodes to determine when data is to be sent or received. However, the details of scheduling time intervals are not specified by the TSCH-mode IEEE802.15.4e standard. In this work, we propose a distributed Optimized Minimum Scheduling Function (OMSF) that is based on the 802.15.4e standard TSCH mode. For this purpose, a distributed algorithm is being implemented to predict the scheduling requirements over the next slotframe, focused on the Poisson model and using a cluster tree topology. As a consequence, it will reduce the negotiation operations between the pairs of nodes in each cluster to decide on a schedule. This prediction allowed us to deduce the number of cells needed in the next slotframe. Clustering decreases, the overhead processing costs that produce the prediction model. So, an energy-efficient data collection model focused on clustering and prediction has been proposed. As a result, the energy consumption, traffic load, latency, and queue size in the network, have been reduced.