Authentication Stage Research Articles

Deep Feed-Forward Neural Network-Based Biometric Authentication System with Biometric Identity and Reputation Score in Blockchain

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346.

Authenticated multi-party quantum key agreement protocol based on cluster states

Quantum key agreement (QKA) is an important cryptographic primitive that plays a pivotal role in private communications. Authenticated QKA plays an important role in QKA. In this paper, we propose an authenticated multiparty QKA scheme based on 4-qubit cluster states entanglement swapping. The scheme is divided into two parts, the first part is the quantum identity authentication stage, and the second part is the QKA stage. In the quantum identity authentication stage, the participants determine that the participant communicating with them is the claimed person through mutual authentication, which can avoid the impersonation attack of the eavesdropper Eve. In the QKA phase, the participants adopt a bidirectional transmission structure with the participation of a semi-trusted third party, and based on the entanglement swapping property of the 4-qubit cluster state, the QKA is finally realized. Detailed security analysis shows that the protocol can resist a variety of internal and external attacks, such as participants attack, entangle-measure attack, and so on.

Continuous identity authentication protocol against quantum attacks in satellite integrated smart grid

AbstractTo address the issue of low efficiency caused by the repeated use of quantum attack resistant static identity authentication methods in a satellite integrated smart grid, this paper proposes a quantum attack resistant continuous identity authentication protocol. First, in the initial authentication stage, in order to reduce computational complexity, the key encryption mechanism in the CRYSTALS‐Kyber algorithm was improved and combined with the NTRU message recovery digital signature scheme to construct a lattice based explicit AKE (Kyber NTRU. AKE), which achieved mutual authentication and negotiated shared tokens. Second, in the continuous authentication stage, incorporating quantum attack resistant tokens into the current algorithm to improve authentication efficiency. The formal analysis results indicate that compared to the weakly forward secure Kyber.AKE in the CRYSTALS‐Kyber algorithm, Kyber‐NTRU.AKE achieves complete forward secrecy, while the non‐formal analysis results demonstrate the security of the continuous authentication phase. Through theoretical analysis and efficiency comparison with Cyber.AKE, the analysis shows that the Cyber‐NTRU.AKE has higher computational and communication efficiency than Cyber.AKE.

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Watermarking is one of the crucial techniques in the domain of information security, preventing the exploitation of 3D Mesh models in the era of Internet. In 3D Mesh watermark embedding, moderately perturbing the vertices is commonly required to retain them in certain pre-arranged relationship with their neighboring vertices. This paper proposes a novel watermarking authentication method, called Nearest Centroid Discrete Gaussian and Levenberg–Marquardt (NCDG–LV), for distortion detection and recovery using salient point detection. In this method, the salient points are selected using the Nearest Centroid and Discrete Gaussian Geometric (NC–DGG) salient point detection model. Map segmentation is applied to the 3D Mesh model to segment into distinct sub regions according to the selected salient points. Finally, the watermark is embedded by employing the Multi-function Barycenter into each spatially selected and segmented region. In the extraction process, the embedded 3D Mesh image is extracted from each re-segmented region by means of Levenberg–Marquardt Deep Neural Network Watermark Extraction. In the authentication stage, watermark bits are extracted by analyzing the geometry via Levenberg–Marquardt back-propagation. Based on a performance evaluation, the proposed method exhibits high imperceptibility and tolerance against attacks, such as smoothing, cropping, translation, and rotation. The experimental results further demonstrate that the proposed method is superior in terms of salient point detection time, distortion rate, true positive rate, peak signal to noise ratio, bit error rate, and root mean square error compared to the state-of-the-art methods.

Strengthening Internet of Things Security: Surveying Physical Unclonable Functions for Authentication, Communication Protocols, Challenges, and Applications

The spectrum of Internet of Things (IoT) applications is vast. It serves in various domains such as smart homes, intelligent buildings, health care, emergency response, and many more, reflecting the exponential market penetration of the IoT. Various security threats have been made to modern-day systems. Cyberattacks have seen a marked surge in frequency, particularly in recent times. The growing concern centers around the notable rise in cloning attacks, persisting as a significant and looming threat. In our work, an in-depth survey on the IoT that employs physically unclonable functions (PUFs) was conducted. The first contribution analyzes PUF-based authentication, communication protocols, and applications. It also tackles the eleven challenges faced by the research community, proposes solutions to these challenges, and highlights cloning attacks. The second contribution suggests the implementation of a framework model known as PUF3S-ML, specifically crafted for PUF authentication in the Internet of Things (IoT), incorporating innovative lightweight encryption techniques. It focuses on safeguarding smart IoT networks from cloning attacks. The key innovation framework comprises three stages of PUF authentication with IoT devices and an intelligent cybersecurity monitoring unit for IoT networks. In the methodology of this study, a survey relevant to the concerns was conducted. More data were provided previously regarding architecture, enabling technologies, and IoT challenges. After conducting an extensive survey of 125 papers, our analysis revealed 23 papers directly relevant to our domain. Furthermore, within this subset, we identified 11 studies specifically addressing the intersection of communication protocols with PUFs. These findings highlight the targeted relevance and potential contributions of the existing literature to our research focus.

Analysis of The Process of Formulation And Publication of The Regulation of The Regency Head Regarding The Allocation of Village Funds in Bogor Regency for The Year 2023

The Regent Regulation (Peraturan Bupati or Perbup) is one type of Regional Head Regulation (Peraturan Kepala Daerah or Perkada) that functions as an implementation tool for higher-level national and provincial laws and regulations. This research is conducted because the Bogor Regency Government has issued a Regent Regulation regarding the Village Fund Allocation (Alokasi Dana Desa or ADD) for the fiscal year 2023, but there has been a delay in its issuance. The research aims to determine whether the process of drafting the Bogor Regency Regent Regulation Number 11 of 2023 concerning the Allocation and Procedures for the Distribution of Village Fund for the Fiscal Year 2023 is in accordance with the Laws and Regulations. The author uses the normative juridical method in this research by obtaining data through literature study and interviews. The data sources used include documents related to the Formation of Laws and Regulations, such as the Ministry of Home Affairs Regulation and other relevant regulations, as well as relevant literature data on the research topic. In addition, interviews were conducted with relevant officials to obtain complete and in-depth information. This research indicates that the formation of regent regulations must follow the mechanisms and procedures stipulated in the laws and regulations, including the stages of drafting, deliberation, guidance/facilitation, determination, numbering, promulgation, authentication, and dissemination. Therefore, procedurally, the process of drafting and issuing Regent Regulation Number 11 of 2023 concerning the Allocation and Procedures for the Distribution of Village Fund for the Fiscal Year 2023 is in line with the provisions of Law Number 12 of 2011 concerning the Formation of Laws and Regulations, as well as the Ministry of Home Affairs Regulation Number 80 of 2015 concerning the Formation of Regional Legal Products.

Development of a Universal Platform for Managing Payment Operations on a Global Scale: Methodology and Implementation Examples

The article discusses the main trends and processes in the field of digital finance. The purpose of the research is to analyze the functioning of electronic payment systems as a key element of the digital financial sector, with a focus on identifying the main threats to user security and developing recommendations to improve their security. The author analyzes the functioning of electronic payment systems as an important segment of the digital finance sector. Statistics on the global electronic payments market and forecast indicators are presented. Main security issues for users of electronic payment systems are listed and described. It is noted that customers conducting transactions through an electronic payment system have to disclose some personal and financial information, which exposes them to the risk of data leakage. The use of electronic payment systems is associated with several risks, which vary depending on technology, provider company, and user awareness. A list of major risks is presented, focusing on the stage of authorization and authentication of the client within the system. Ways, methods, and technologies to enhance client security during entry into the payment system, as well as main methods of ensuring security (secure Internet connection, client identification and access control, technical protection, payment system certification), are discussed. Advantages and disadvantages of password-based technologies are presented, including one-time passwords (OTPs), single sign-ons (SSOs), OAuth, OpenIDs, One Identity, and One Key, and the Cyber Source platform.

Empowering Democracy: A Robust and Secure Online Election System

Every democracy and organization’s hallmark are the voting system. The absentee ballot which India has been using have proved to be defective and even ineffective since they require a large manpower, taking longer processing time for the publication of results. Accordingly, to eliminate such flaws from the system, these amendments are implemented in the systems that could circumvent these considerable disadvantages. Physical presence as a criterion is also eliminated by the new method, hence decreasing overall complexity. The country has been undergoing significant changes since 2014 especially the use of online voting to control and manage elections. The system may help to achieve a better performance and enhance the overall voter numbers. On the one hand, it has a positive side in terms of protecting privacy and providing authentic information. The primary points describing security include minimization emerges as everyone’s motive. In our research paper, we conducted an analysis of this system that involves three stages of authentication for guaranteeing the reliability and validity are utilized in the entire voting procedure. A process begins with voter card verification, then comes the second and third level- Aadhar verification and face verification respectively.

Joint Heterogeneous PUF-Based Security-Enhanced IoT Authentication

This study proposes a novel authentication scheme that provides enhanced security for Internet of Things (IoT) applications by integrating a radio frequency (RF) physical unclonable function (PUF) with a device PUF. While traditional PUF-based authentication methods are lightweight, they are vulnerable to replay attacks because challenge-response pairs (CRPs) are exposed to adversaries over wireless channels. To prevent CRP exposure, we propose a joint scheme that integrates the physical layer features of wireless channels with those of the device PUF. Our authentication process consists of two stages: an enrollment stage and an authentication stage. During the enrollment stage, the physical features of the static random-access memory (SRAM) in the IoT devices are shared with the server, which generates a hashing model using the amplitudes of channel state information (CSI) as the RF-PUF and transfers it to the IoT device. In the authentication stage, the server and IoT devices exchange pilot signals to estimate the shared CSI. Both parties then generate the challenge information using the hashing model, which depends on the CSI. The challenge information is mapped to the response of the device PUF as the cryptographic key. If both parties are legitimate, the proposed model is updated through CSI amplitudes. We evaluated the proposed authentication protocol using a testbed based on Raspberry Pi and acquired a CSI dataset and SRAM-PUF using universal software radio peripheral and Arduino, respectively. Numerical results demonstrate that our method effectively defends against diverse attacks, even in critical CRP exposure scenarios.

SearchAuth: Neural Architecture Search-based Continuous Authentication Using Auto Augmentation Search

Mobile devices have been playing significant roles in our daily lives, which has made device security and privacy protection extremely important. These mobile devices storing user sensitive and private information, therefore, need rigorous user authentication mechanisms. In this article, we present SearchAuth, a novel continuous authentication system on smartphones exploiting a neural architecture search (NAS) to find an optimal network architecture and an auto augmentation search (AAS) to more effectively train the optimal network along with the best data augmentation policies, by leveraging the accelerometer, gyroscope, and magnetometer on smartphones to capture users’ behavioral patterns. Specifically, SearchAuth consists of three stages, i.e., the offline stage, registration stage, and authentication stage. In the offline stage, we utilize the NAS on sensor data of the accelerometer, gyroscope, and magnetometer to find an optimal network architecture based on the designed search space. With the optimal network architecture, namely, NAS-based model, the AAS automatically optimizes the augmentation of the input data for more effectively training the model that is for feature extraction. In the registration stage, we use the trained NAS-based model to learn and extract deep features from the legitimate user’s data, and train the LOF classifier with 55 features selected by the PCA. In the authentication stage, with the well-trained NAS-based model and LOF classifier, SearchAuth identifies the current user as a legitimate user or an impostor when the user starts operating a smartphone. Based on our dataset, we evaluate the performance of the proposed SearchAuth, and the experimental results demonstrate that SearchAuth surpasses the representative authentication schemes by achieving the best accuracy of 93.95%, F1-score of 94.30%, and EER of 5.30% on the LOF classifier with dataset size of 100.

Utilizing Multi Stage Attestation for Secure Cloud Data Retrieval

Abstract: The Advancement in data storage has taken a major leap. Due to Current trending technologies that have been taken place many companies find it convenient to store the data in Cloud. Cloud Computing refers to the storage, networking, operations, etc... in a single place with a pay as you use pricing. Due to these kind of numerous advantages companies use cloud to store the data. Now the problem with the cloud is there is no specific Authentication measures that have been taken place while storing and retrieving the data. So, we are proposing a Multi Stage Authentication(MSA) method to encrypt and decrypt the data with the help of a Image that acts a Secondary stage of Authentication. As there is a three-step process that goes on when the data is transferring the whole process becomes reluctant to any data leaks or data loss.

Palmprint authentication using symbolic aggregate approximation features with improved enhancement techniques

The palmprint enhancement is a pre-processing stage of palmprint authentication. The accuracy of the recognition rate can be improved by incorporating effective palmprint enhancement methods. So far, only a little work has been done in palmprint enhancement. Little attention is given to incorporate the enhancement techniques in palmprint authentication system to achieve performance improvement. This work presents robust enhancement methods which provide better performance and accuracy. Palmprint enhancement using curvelet and Recursive Histogram Equalization (RHE) overcomes the drawbacks of the existing systems and makes palmprint recognition simpler and more accurate with enhanced palm image as input. Palmprint recognition system uses the Symbolic Aggregate Approximation (SAX) features from the enhanced palmprint image. The recognition rate is optimum when both curvelet and RHE methods are used for enhancement of palmprint. Here we compared different types of enhancement methods and we have obtained maximum recognition rate with a combination of recursive histogram equalization and curvelet transform. The performance of the palmprint authentication is measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Total Success Rate (TSR). The comparison of recognition rate with enhancement is done using minimum distance classifier, Support Vector Machine, Random Forest and Bayesian. Random Forest classifier provides better results.

Security Access Control Method for Wind-Power-Monitoring System Based on Agile Authentication Mechanism

With the continuous increase in the proportion of wind power construction and grid connection, the deployment scale of state sensors in wind-power-monitoring systems has grown rapidly with an aim on the problems that the communication authentication process between the wind turbine status sensor and the monitoring gateway is complex and the adaptability of the massive sensors is insufficient. A security access control method for a wind-power-monitoring system based on agile authentication mechanism is proposed in this paper. First, a lightweight key generation algorithm based on one-way hash function is designed. The algorithm realizes fixed-length compression and encryption of measurement data of any length. Under the condition of ensuring security, the calculation and communication cost in the later stage of authentication are effectively reduced. Second, to reduce the redundant process of wind turbine status sensor authentication, an agile authentication model of wind turbine status sensor based on a lightweight key is constructed. Constrained by the reverse order extraction of key information in the lightweight keychain, the model can realize lightweight communication between massive wind turbine status sensors and regional gateways. Finally, the proposed method is compared and verified using the wind turbine detection data set provided by the National New Energy Laboratory of the United States. The experimental results show that this method can effectively reduce the certification cost of a wind-power-monitoring system. Additionally, it can improve the efficiency of status sensor identity authentication and realize the agility and efficiency of the authentication process.

Confidentiality-Preserving Edge-Based Wireless Communications for Contact Tracing Systems

This paper addresses two issues of a contact-tracing system with edge-based wireless communication techniques: to locate close contacts and to provide confidentiality-preserving communications. In this paper, we propose the Confidentiality-Preserving Contact-Tracing (CPCT) system with multiple wireless edge-based nodes. The CPCT system consists of three stages: registration, authentication, and confidentiality-preserving communication stages. We propose two Physical-Layer Authentication (PLA) schemes for the authentication stage of the CPCT system: the Coordinate-based Location PLA (CLP) scheme using the estimated coordinates and the Time-of-Arrival (ToA) based Location PLA (TLP) scheme using the estimated ToAs. We propose a distributed encryption scheme for the confidentiality-preserving communication stage of the CPCT system named the Distributed Channel Impulse Response (CIR)-based Encryption (DCE) scheme. We provide the theoretical analysis of the proposed schemes and derive their closed-form expressions. We implement the proposed schemes and conduct extensive performance comparisons through simulations. We observe that the theoretical results perfectly match the corresponding simulation results. Moreover, the proposed PLA schemes provide higher authentication performance than the prior PLA scheme, while the proposed encryption scheme provides higher confidentiality performance than the prior encryption scheme.

Bio-Touchpass: Efficient Password Mechanism to Overcome Spyware Attacks

Abstract: This is a work exaggerated historical authentication structures supported the personal identification number (PIN) and one time password (OTP) through the fusion of biometric statistics as a second stage of person authentication. In this project, users can draw every digit of the parole on the touch screen device instead of usual method. A whole exploration of this bio metric gadgets is to drop a relative discriminative energy of every written notation and consequently the hardness once growing the size of the parole and consequently the variety of approached samples. The advanced eBioDigit data, that includes on line written notation from zero to nine, has been non heritable manipulation of the finger as enter on an android device. The data is employed within the experiments in accordance’s in the course of this work and it is on the market at the side of benchmark. Eventually, we have a tendency to discuss precise important points for the education of our innovation strategy on the present PIN and OTP systems, reaching outcomes with equal error rates (EERs) ca. 4.0% once the assaulter is aware of the parole. These outcomes inspire the practice of our innovation strategy in contract to historical PIN and OTP systems anywhere the assault would have a hundred percent success amount underneath steady faux situation. Keyword: Mobile User Authentication; Password; Biometrics; Handwriting; PIN; OTP; Touchscreen; Touch Interaction

Proposing a security system for the VPN through design and implementation of a scheme for android and IOS mobiles based on two-factor authentication

A virtual private network (VPN) is a network, but a virtual network means that it creates a virtual bridge between the user and the server located somewhere across the world, and this network is private because to connect the connection with it you must have an account and password. The idea of VPN arose to protect its communications from industrial espionage, because there are very easy ways to penetrate a network and steal the information circulating in it. Data transmission encryption protocols and tunneling systems are used in order to secure the transfer of information between two points back and forth, so their data is encrypted and protected. Hacking the VPN is a very dangerous thing because of the importance and privacy of the data. Therefore, special systems must be provided for the VPN to suit the network's work scenarios with high security. In our work, we proposed a private authentication system for the VPN network that provides high security with fast execution and reliability based on two factors authentications: Using the varieties of authentications method, robust VPNs screen everybody who will tries to sign in. An authentication system was designed based on a special user interface that provides an easy environment for choosing two passwords in two different stages of registration and authentication. The proposed system was implemented on Android and iOS devices. The proposed system was evaluated through experiments with 720 participants with 3600 system entry processes, then the proposed system was tested in terms of its ability to break and resistance to different types of attacks. Where it was tested by 1900 attacks at different times with participants who were randomly selected from the main sample of participants during the implementation period of 30 days through using the modern types of mobile devices.

Novel Robust Photoplethysmogram-Based Authentication

As the tremendous advances in the biosensing technology, the biometric authentication approach has been adopted to replace the conventional password-based systems. Due to their harmless nature, photoplethysmogram (PPG) related applications have been quite intriguing in recent years. PPG can measure volumetric blood-flow changes in the peripheral circulation. Because PPG signals are very easy to acquire, compared to other biometrics (fingerprints, iris/retina, etc.), especially by low-cost wearable electronic devices, they are widely adopted to measure heart rates. In this paper, we devise a novel robust PPG based authentication system, which is capable of continuously authenticating the user instead of one-time authentication as carried out by the conventional techniques. This new system consists of preprocessing and filtering, motion artifact (MA) removal, template and feature extraction, and training. The associated multiwavelet-based feature extraction mechanism facilitates more reliable features than the conventional scalar-wavelet schemes so that it allows the learning model to better distinguish users. The associated autoencoder perfectly transforms the input PPG signal into the latent space. Finally, any distance measure can be adopted to classify or authenticate users. In order to build a sufficiently large dataset, we have combined three public datasets and our own dataset collected by ourselves locally together to create a new dataset consisting of 120 subjects totally. We have already built the hardware prototype successfully for real-time training data collection and real-time authentication. The equal-error-rate at the authentication stage reaches 5.5% and the identification accuracy reaches 98% at the identification stage.

A two‐layer attack‐robust protocol for IoT healthcare security

The majority of studies in the field of developing identification and authentication protocols for Internet of Things (IoT) used cryptographic algorithms. Using brain signals is also a relatively new approach in this field. EEG signal-based authentication algorithms typically use feature extraction algorithms that require high processing time. On the other hand, the dynamic nature of the EEG signal makes its use for identification/authentication difficult without relying on feature extraction. This paper presents an EEG-and fingerprint-based two-stage identification-authentication protocol for remote healthcare, which is fast, robust, and multilayer-based. A modified Euclidean distance pattern matching method is proposed to match the EEG signal in the identification stage due to its dynamic nature. The authentication stage is also an optimized method with the Genetic Algorithm (GA), which utilizes a modified Diffie–Hellman algorithm. Due to the vulnerability of the Diffie–Hellman algorithm to different types of attacks, the parameters used for this algorithm are extracted from the fingerprint and the EEG signal of the patient to provide a fast and robust authentication method. The proposed method is evaluated using data from patients with spinal cord injuries. Simulating results demonstrated high identification and authentication accuracy of the proposed method. Furthermore, it is extremely fast and efficient.

Cancelable Multimodal Biometrics Based on Chaotic Maps

Biometric authentication systems raise certain concerns with regard to security, violation of privacy, and storage issues of biometric templates. This paper proposes a protection approach of biometric templates storage in a multimodal biometric system while ensuring both the cancelability of biometric templates and the efficiency of the authentication process. We propose applying a chaotic maps-based transform on the biometric features to address the cancelability issue. We used Logistic map and Torus Automorphism to generate cancelable biometric features of the face and fingerprint minutia points, respectively. Both transformed features would be concatenated and saved in the database of the system instead of the original features. In the authentication stage, the similarity scores of both transformed face and fingerprint templates are computed and fused using the weighted sum rule. The results of the experimentation, conducted using images from the ORL face and FVC2002 DB1 fingerprint databases, demonstrated the higher performance of the proposed approach achieving a genuine accept rate equal to 100%. Moreover, the obtained results confirmed the soundness of the proposed cancelable technique to satisfy the biometric systems’ requirements (i.e., security, revocability, and diversity).

Using recurrent neural networks for continuous authentication through gait analysis

Abstract This letter presents a novel framework for continuous user authentication of mobile devices based on gait analysis, exploiting inertial sensors and Recurrent Neural Network for deep-learning based classification. The proposed framework handles all the continuous authentication stages, starting from data collection to data preprocessing, classification, and policy enforcement. The letter will emphasize the data analysis aspects, discussing the methodologies used to improve the quality of classification, including data augmentation and a sliding window interval approach for improved training. Furthermore, will be discussed the enforcement, which is based on the Usage Control paradigm for continuous policy enforcement. A set of real experiments will demonstrate the effectiveness and efficiency of the proposed framework.