Secure Identification Research Articles

The security protection technology and architectural design of distributed generation scheduling control systems

This paper delves into the pivotal role of Distributed Generation Scheduling Control Systems in the power grid and addresses critical network security issues. It analyzes key concerns like data collection, security monitoring, and device management during distributed generation integration, proposing a comprehensive set of security technologies and architectural designs. The research covers network security identification for these systems and secure access and management for distributed generation terminals. The goal is to ensure the security and reliable operation of Distributed Generation Systems within the power grid.

A Pairing-free Provable Secure and Efficient Identity-based Identification Scheme with Anonymity

In this paper, we propose a Blind Identity-Based Identification (Blind IBI) scheme based on the Guillou-Quisquater (GQ) scheme. Our proposed scheme combines the benefits of traditional Identity-Based Identification (IBI) schemes that can authenticate a user's identity without relying on a trusted third party with the Blind Signature (BS) scheme that provides anonymity. As a result, the proposed scheme assures absolute user privacy during the authentication process. It does not rely on a third party, yet the verifier can still be assured of the user's identity without the user actually revealing it. In our work, we show that the proposed scheme is provably secure under the random oracle model, with the assumption that the one-more-RSA-inversion problem is difficult. Furthermore, we demonstrate that the proposed scheme is secure against passive, active, and concurrent impersonation attacks. In conclusion, the proposed scheme is able to achieve the desired blindness property without compromising the security of the GQ-IBI scheme it is based upon.

Innovations in the Security of Banking Operations

The contemporary changing technological environment forces all economic units to go through a digital transformation. The banking industry is in the epicenter of this change. Credit institutions seek to adapt a business model that is increasingly customer-oriented, providing new services with greater transparency and levels of security. The growing demand for more effective protection against fraud, phishing attacks, theft of identities and ensuring better levels of security in a timely manner is increasingly central topic in retail banking. The issue of security, as in the territory of bank branches, ATM-devices, as well as in the provision of online and mobile services, arouses the interest of credit institutions to the use of biometric identification technology. Biometric technologies are becoming an indispensable part of the means of personal verification and secure identification activities in banking institutions.

Spatial multiplexing encryption with cascaded metasurfaces

Recent years have witnessed rapid progresses in information encryption by harnessing different dimensions of light, yet, pushing the capacity of encrypted information is an endless goal. Meta-holographic encryption introduces an efficient method in optical information encryption. Here, we propose a spatial multiplexing encryption technique using cascaded metasurfaces, allowing multiple users to simultaneously access independent encryption information. By employing cascaded metasurfaces, cipher images and corresponding decryption keys can be transferred to unique spatial hologram coordinates for each user, ensuring secure identification of encrypted information. We demonstrate the feasibility of six encrypted channels and analyze the sensitivity to holography spatial location, revealing a low correlation among different channels. Our findings highlight the potential of cascaded metasurfaces in spatial multiplexing encryption, paving the way for high-volume optical information encryption.

The Effects of Cost Implications in the Adoption of Biometric Application Banks in Eldoret Town, Kenya

Biometric technology utilizes biological and behavioral characteristics to automatically identify individuals. The paper was guided by the question on how does cost implications affect for adoption of biometric application in authorization and identification in banks in Eldoret Town. Cost of implications does not influence for adoption of biometric application in authorization and identification in banks in Eldoret Town. Thus, this study will help government agencies, financial institutions and other corporate business in adopting biometric systems. The research indicated cost implications affects biometric application in authorization and identification in cyber security in banks where biometric applications in banks have insufficient technical infrastructure hence affecting inefficiency and ineffectiveness of bank operations. In conclusion, the research indicated that cost implications are a major indicator that influenced biometric application in authorization and identification in cyber security in commercial banks in Kenya.

Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network

Security identification solutions against WLAN network attacks according to straightforward digital detectors, such as SSID, IP addresses, and MAC addresses, are not efficient in identifying such hacking or router impersonation. These detectors can be simply mocked. Therefore, a further protected key uses new information by combining these simple digital identifiers with an RF signature of the radio link. In this work, a design of a convolutional neural network (CNN) based on fingerprinting radio frequency (RF) is developed with computer-generated data. The developed CNN is trained with beacon frames of a wireless local area network (WLAN) that is simulated as a result of identified and unidentified router nodes of fingerprinting RF. The proposed CNN is able to detect router impersonators by comparing the data pair of the MAC address and RF signature of the received signal from the known and unknown routers. ADAM optimizer, which is the extended version of stochastic gradient descent, has been used with a developed deep learning convolutional neural network containing three fully connected and two convolutional layers. According to the training progress graphic, the network converges to around 100% accuracy within the first epoch, which indicates that the developed architecture was efficient in detecting router impersonations.

Advancing Nutrition and Dietetics Research in Global Food and Nutrition Security: A Roundtable Meeting Report

Food and nutrition security remains a relevant issue globally, impacting nutritional status and other health outcomes. This is further complicated by various environmental factors that impact stable access to, availability of, and utilization of nutritious foods. Nutrition and dietetics practitioners play an important role in the identification and treatment of food and nutrition security and are also well positioned to advance research that can support food and nutrition security solutions. To address this important issue, the Academy of Nutrition and Dietetics’ Council on Research convened a Global Food and Nutrition Security Research Task Force (Task Force). To leverage existing information and expertise in this area and identify the need for future evidence, the Task Force hosted a virtual roundtable with key internal and external stakeholders. This 2-day event included discussions on research gaps, potential entry points for nutrition and dietetics practitioners, and important equity considerations in the area of food and nutrition security research. The identified research gaps included the need for standardized terminology for consistent data collection, the need for validated screening and assessment tools that can be used across settings and also assess diet quality, additional translational and implementation science research, multi-sectoral and multi-pronged approaches, interdisciplinary collaboration with community partners, incorporation of research into policy development, and additional evidence on food systems approaches to target food and nutrition security. To more clearly identify the entry points for practitioners, five examples from various countries were included to identify food and nutrition security issues and how nutrition and dietetics practitioners can be involved in research to address food and nutrition security. The Task Force would like this information to inform a research agenda and be leveraged by the larger scientific community to drive future funding and research opportunities for food and nutrition professionals on this topic.

Soft computing approach for feature extraction of palm biometric

The method for developing a secure and reliable identification system relies on the use of biometrics. In this case, the palm vein is used as a security measure. The proposed system is powered by a convolutional neural network, which is a type of neural network that is commonly utilized for image recognition. The palm vein's visual features are extracted using a convolutional neural network. This method can improve the recognition rate and its performance parameters. The results of an experiment conducted with this method were better than those obtained with conventional techniques.

A robust THD based communication-less protection method for electrical grids with DGs

In this paper, a protection method with two cascaded algorithms is proposed to face fault events in Electrical Distribution Systems (EDSs). The first algorithm uses the Total Harmonic Distortion (THD), the estimates of the amplitude voltages, and the zero-sequence component of the grid voltages for defining an adaptive algorithm that provides a secure detection and identification process for the faults in the grid network. The second algorithm uses the behavior of the positive and negative-sequence components of the grid voltages during the fault events to locate and isolate the faults. Its main merit is that it doesn’t need to employ communication lines for transmitting trip signals between the protective devices (PDs) of the system. A Second Order Generalized Integrator (SOGI) is used for obtaining the estimated variables and sequence components, which allows fast detection with a low computational burden. The harmonic content existing prior to the fault is taken into consideration for not affecting the detection of the fault. The performance of the protection method’s is evaluated experimentally and by using simulations in MATLAB/Simulink and employing the IEEE 9-bus standard ring system. The proposed method shows the capability for detecting, identifying, and isolating different fault types with different DG penetrations and fault resistances in different locations of the proposed network and even when the grid configuration is changed. The method is robust to the possible harmonic distortion of the network.

People Identification based on Geometric Face Features for Cloud Services

Recently, the services provided by cloud computing play an important role in many areas of life. The security component represents the major challenge that hinders the adoption of cloud services in banking, healthcare and other transactions. In this paper, secure identification approach is proposed to use cloud services by recognizing authorized persons based on facial features. In the proposed approach, the face image is sent to the cloud side without any processing. When it is received in the cloud side, the image is processed and the geometric features are extracted using moment features extractor. The features of the face are used during the matching phase by using two different classifiers to make the final decision to grant authorization to access cloud services. The experimental results showed the efficiency and accuracy of the proposed approach. The performance is test using many facial images with online applications. Where an overall accuracy level of approximately 96% was obtained.

Lanthanide‐FRET Molecular Beacons for microRNA Biosensing, Logic Operations, and Physical Unclonable Functions

AbstractTime‐resolved or time‐gated (TG) biosensing and bioimaging with luminescent lanthanide probes and Förster resonance energy transfer (FRET) have significantly advanced bioanalytical chemistry. However, the development of lanthanide‐based molecular beacons (MBs) has been rather limited. Here, we designed DNA stem‐loop MB probes against two different microRNAs (miR‐21 and miR‐27b) using Tb and Eu FRET donors and quenching (BHQ2) and fluorescent (Cy3) FRET acceptors. Limits of detection down to 190 pM and duplexed miR‐21/miR‐27b quantification at low nanomolar concentrations with Tb‐BHQ2 and Eu‐BHQ2 TG‐FRET MBs demonstrated the versatility and high analytical performance of lanthanide‐based MBs. The particular donor‐acceptor distances in the Tb‐Cy3 MB resulted in inverted nucleic acid target concentration‐dependent TG PL intensities in short (e. g., 0 to 40 μs) and long (e. g., 0.1 to 2.1 ms) TG detection windows after pulsed excitation. We showed that this specific feature of our TG‐FRET MBs can be adapted to the design of molecular logic devices (NOR, OR, NAND, AND, XNOR, XOR, IMPLEMENT, and INHIBIT). Moreover, the almost unlimited choice of TG detection windows and the distinct spectral features of Tb and Cy3 over a broad visible spectral range could be exploited to devise biophotonic physical unclonable functions for highly secure authentication and identification. Our study manifests the versatility of lanthanides for advanced biophotonic applications.

Incest and the Maintenance of Episcopal Consensus in the Vita s. Albini

Abstract: Usually dated among the earliest of Venantius Fortunatus's hagiographical works, the Vita s. Albini describes a dispute between Bishop Albinus of Angers and his episcopal colleagues at an ecclesiastical council. At the council, the saintly bishop was forced to lift an order of excommunication that he had placed upon an unnamed layperson guilty of incest. While modern scholars occasionally have attempted to identify this event with known Gallo-Frankish councils of the mid-sixth century, the information supplied by the Vita does not, in fact, support a secure identification, leaving open the possibility that the confrontation took place at an otherwise-unknown council ca. 536/7–42. Further complicating previous efforts to contextualize the dispute has been a general neglect of the narrative's compositional context. Writing in the aftermath of a case of royal incest that occurred in late 567 c.e., Fortunatus seems to have had this specific case in mind in composing his narrative, utilizing a conflict by then safely in the past as a literary device to address a current and controversial problem: the pressure exerted by secular elites to divert bishops from their corporate moral imperatives. The hagiographer cast Albinus himself in a role analogous to that of Bishop Germanus of Paris, who recently had risked martyrdom in excommunicating King Charibert I, following a failed conciliar effort to convince the king to end his uncanonical marriage. Fortunatus thus explored through Albinus's experiences the real difficulties facing bishops who were expected as a consequence of their membership in a common ordo to serve as spiritual counselors to the powerful.

QR-PUF: Design and Implementation of a RFID-Based Secure Inpatient Management System Using XOR-Arbiter-PUF and QR-Code

In recent years, Physical Unclonable Functions (PUFs) have played a major role in providing low-cost physical security for IoT devices such as Radio Frequency Identification (RFID) tags. PUFs take advantage of the physical properties of the device to build unique security primitives that can be used by authentication mechanisms. Meanwhile, the security and convenience of QR codes for device authentication on mobile devices has been widely recognized. The point-to-point communication makes it less vulnerable to interception and analysis by adversaries. In this article, we propose a <i>new</i> RFID-based secure inpatient management system for identifying a legitimate patient. Our proposed system uses an XOR Arbiter PUF to generate a secret key-stream and then uses the key-stream to construct a secure QR code for secure identification. Also, since PUFs are vulnerable to machine learning attacks, we propose a modeling attack resilience obfuscation framework to enhance the security of the proposed protocol. Security analysis of the proposed scheme using ProVerif shows that the scheme is effective against a variety of imperative attacks on RFID devices. To show the applicability of the proposed scheme, we also provide a case study of an inpatient management system in hospitals.

Preserving Privacy: How Governments and Digital Services Can Harness Zero-Knowledge Proofs for Secure Identification

Preserving Privacy: How Governments and Digital Services Can Harness Zero-Knowledge Proofs for Secure Identification

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition.

The unseen host galaxy and high dispersion measure of a precisely localized fast radio burst suggests a high-redshift origin

ABSTRACT FRB 20210912A is a fast radio burst (FRB), detected and localized to subarcsecond precision by the Australian Square Kilometre Array Pathfinder. No host galaxy has been identified for this burst despite the high precision of its localization and deep optical and infrared follow-up, to 5σ limits of R = 26.7 mag and Ks = 24.9 mag with the Very Large Telescope. The combination of precise radio localization and deep optical imaging has almost always resulted in the secure identification of a host galaxy, and this is the first case in which the line of sight is not obscured by the Galactic disc. The dispersion measure of this burst, DMFRB = 1233.696 ± 0.006 pc cm−3, allows for a large source redshift of z &amp;gt; 1 according to the Macquart relation. It could thus be that the host galaxy is consistent with the known population of FRB hosts, but is too distant to detect in our observations (z &amp;gt; 0.7 for a host like that of the first repeating FRB source, FRB 20121102A); that it is more nearby with a significant excess in DMhost, and thus dimmer than any known FRB host; or, least likely, that the FRB is truly hostless. We consider each possibility, making use of the population of known FRB hosts to frame each scenario. The fact of the missing host has ramifications for the FRB field: even with high-precision localization and deep follow-up, some FRB hosts may be difficult to detect, with more distant hosts being the less likely to be found. This has implications for FRB cosmology, in which high-redshift detections are valuable.

More identified so less envious? On the links between different types of national identity and in-group envy

ABSTRACT We investigate relations between benign and malicious in-group envy and the two types of national identity (i.e. secure national identification vs. national narcissism). In two studies (Ns = 1000 and 633), we found that secure national identification was negatively linked to malicious envy, while national narcissism was positively related to both malicious and benign envy. In Study 2, we additionally analyzed how in-group envy and two types of national identity shape in-group altruism. We found that low malicious envy significantly mediated the relationship between secure identification and in-group altruism. We discuss the role of envy in shaping the links between secure (vs. narcissistic) identity and positive intragroup attitudes.

Security and Privacy Determinants in Biometric Application in Cyber Security in Banks in Eldoret Town, Kenya

Biometric technology has been seen as a driving force for the future direction of strong authentication. Although Biometric has been deemed by many scholars as secure method of identification and verification, few banks in Kenya have implemented the use of biometric features such as fingerprints for customer and systems user identification. There are some that put in place the system but failed to fully utilize the system.What could be the key determinants to successful implementation or adoption of the biometric systems for identification and verification. What are the elements that makes it possible for a banking institution to effectively adopt and use the biometric systems for identification and verification. The study sought to determine the effect of security and privacy in biometric application for authorization and identification in cyber security in banks in Eldoret Town. The study was guided by the contingency theory, diffusion innovation theory, and unified theory of acceptance and use of technology. A descriptive survey design was used. The study comprised of 548 employees from 30 commercial banks in Eldoret Town. The study concludes that security and privacy influence biometric application in authorization and identification in cyber security in banks in Kenya. The study recommended that banks should provide convenient and more secured banking services to customers. Additionally, the study recommended a model for evaluation of biometric system before its implementation. The model will be used to ensure successful implementation and application of the system. The determinants are: security and privacy. Biometric technology in banks needs to integrate with the existing traditional security system which will help the banks the level of authorization and identification cyber security.

An End-to-End Transfer Learning Framework of Source Recording Device Identification for Audio Sustainable Security

Source recording device identification poses a significant challenge in the field of Audio Sustainable Security (ASS). Most existing studies on end-to-end identification of digital audio sources follow a two-step process: extracting device-specific features and utilizing them in machine learning or deep learning models for decision-making. However, these approaches often rely on empirically set hyperparameters, limiting their generalization capabilities. To address this limitation, this paper leverages the self-learning ability of deep neural networks and the temporal characteristics of audio data. We propose a novel approach that utilizes the Sinc function for audio preprocessing and combine it with a Deep Neural Network (DNN) to establish a comprehensive end-to-end identification model for digital audio sources. By allowing the parameters of the preprocessing and feature extraction processes to be learned through gradient optimization, we enhance the model’s generalization. To overcome practical challenges such as limited timeliness, small sample sizes, and incremental expression, this paper explores the effectiveness of an end-to-end transfer learning model. Experimental verification demonstrates that the proposed end-to-end transfer learning model achieves both timely and accurate results, even with small sample sizes. Moreover, it avoids the need for retraining the model with a large number of samples due to incremental expression. Our experiments showcase the superiority of our method, achieving an impressive 97.7% accuracy when identifying 141 devices. This outperforms four state-of-the-art methods, demonstrating an absolute accuracy improvement of 4.1%. This research contributes to the field of ASS and provides valuable insights for future studies in audio source identification and related applications of information security, digital forensics, and copyright protection.

An efficient chain code for access control in hyper ledger fabric healthcare system

The emergence of eHealth has been instrumental in enhancing healthcare systems, offering improved health services to diverse patients with the aid of cutting-edge technologies. The safe and effective collection, transmission, and accessibility of electronic health records (EHRs) using modern information and communication technologies (ICTs) is essential for realizing the goals of eHealth. However, the use of EHRs by healthcare practitioners creates the risk of data breaches and jeopardizes patient confidentiality. Fortunately, smart contracts and blockchain technology offer effective EHR access control techniques that can enable secure client identification, authentication, and authorization, enhancing the security of EHRs.So, this paper brings effective Blockchain-based solutions using Hyperledger fabric for providing privacy to EHRs. An effective deployment architecture and chain code were generated for design and access control. In this case, the utilization of Hyperledger Fabric as the Blockchain technology introduces a new consensus mechanism, enabling efficient scalability and safeguarding anonymity. By employing Hyperledger Fabric for framework development, the system achieves its objectives. Experimental results are carried out for analyzing the system performance and also computational tests.