Current Authentication Research Articles

A decentralized authentication scheme for smart factory based on blockchain

The Industrial Internet of Things (IIoT) is recognized as one of the revolutionary technologies driving smart manufacturing and improving productivity. As manufacturing processes grow increasingly intricate, the entire manufacturing ecosystem encompasses multiple managed IoT domains. Within this highly interconnected environment, devices from diverse domains must collaborate, leading to considerable apprehensions about the security and privacy of device-to-device communications. Current authentication methods encounter several challenges. Traditional authentication schemes overly rely on trusted third parties, rendering them susceptible to external attacks or internal spoofing. This susceptibility gives rise to a range of security and privacy concerns. In response to these challenges, this paper aims to contribute to a more secure and efficient service scheme for smart factories by devising a blockchain-based distributed IoT architecture. The proposed scheme introduces a federated blockchain to establish trust among different domains, thereby enabling secure connections between devices in distinct domains. Through security analysis, it is proved that the proposed authentication scheme has integrity, mutual authentication, scalability, and resistance to four attacks. Furthermore, efficiency analysis experiments show that our scheme is feasible for smart factories, and as the number of peer nodes increases, the performance and efficiency of the blockchain network become better.

ANEL: a novel efficient and lightweight authentication scheme for enhancing security in Vehicular Ad Hoc Networks (VANETs) using elliptic curve cryptography

Vehicular Ad-hoc Networks (VANETs) offer immense potential for revolutionizing modern transportation systems through real-time communication and interaction between vehicles and roadside infrastructure. These networks can significantly enhance traffic management, improve safety, and support various vehicular services. However, ensuring secure communication in VANETs remains a critical challenge due to the highly sensitive nature of the data exchanged, such as location and personal information. Current authentication schemes, including the ANEL scheme, exhibit vulnerabilities like data interception and tracking risks. In this paper, we propose a novel, efficient, and lightweight authentication scheme that leverages elliptic curve cryptography (ECC) and message mapping techniques to address these security issues. The proposed solution strengthens encryption, enhances privacy protection, and mitigates risks such as unauthorized access and man-in-the-middle attacks. Furthermore, the scheme offers scalability and distinguishes between regular and priority users, making it adaptable to various VANET applications. This approach significantly improves the overall security and privacy framework of VANET systems while ensuring the efficient use of computational resources in vehicular environments.

Main Primitive and Cryptography Tools for Authentication in VANET Environment: Literature Review

Vehicular ad hoc networks (VANETs) provide the potential to improve transportation efficiency by facilitating the sharing of traffic information among vehicles. Acceptance of VANET depends on communication speed and accuracy as well as privacy protection guaranteeing an individual's safety. Vehicle authentication is necessary to ensure message correctness. This necessitates the implementation of an effective privacy-preserving authentication scheme, as well as the need for both secrecy and timebound delivery of messages. Various privacy-preserving authentication schemes have been suggested to guarantee the integrity of messages in communications. However, most of the schemes are not able to solve issues related to computing costs, communication, security, privacy, threats, and vulnerabilities. In this review, we focus on cryptographic strategies that are suggested to accomplish privacy and authentication, such as identity-based, public key cryptography-based, pseudonym-based, and blockchain-based schemes. We provide a thorough analysis of schemes, including their categorizations, advantages, and drawbacks. The study demonstrates that the majority of current authentication techniques necessitate trusted authorities that lack transparency in their operations. Additionally, authentication process incurs substantial computational and communication overhead, leading to a considerable impact on the timely delivery of messages. More efforts are required to enhance the development of efficient authentication schemes in VANETs.

Fraudguard: Innovations in Financial Transaction Security

Abstract: This paper discusses the importance of improving authentication and detecting fraud in mobile payments and financial technologies. It examines data analytics, new technologies, and current authentication methods, emphasizing how they enhance security. Taking precautionary steps is crucial in reducing the extensive risks linked to fraud. This study creates a strong structure for safeguarding financial transactions, impacting the course of financial technology, and ensuring financial integrity and user trust amidst evolving payment methods and online threats through the analysis of case studies, industry regulations, and top practices.

Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet

ABSTRACT This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based learning-based DetectNet–(IL-DN), privacy-preserving techniques, and blockchain technology. Motivated by the escalating need for robust authentication methods in the face of evolving cyber threats, the research aims to overcome the struggle between accuracy and user privacy inherent in current authentication methods. The proposed system swiftly and accurately identifies users using multimodal biometric data through IL-DN. To address privacy concerns, advanced techniques are employed to encode biometric data, ensuring user privacy. Additionally, the system utilizes blockchain technology to establish a decentralized, tamper-proof, and transparent authentication system. This is reinforced by smart contracts and an enhanced Proof of Work (PoW) mechanism. The research rigorously evaluates performance metrics, encompassing authentication accuracy, privacy preservation, security, and resource utilization, offering a comprehensive solution for secure and privacy-enhanced user authentication in cloud-based environments. This work significantly contributes to filling the existing research gap in this critical domain.

Temporal ECDSA: A timestamp and signature mask enabled ECDSA algorithm for IoT client node authentication

Internet of Things (IoT) networks are vulnerable to various security threats, especially in client authentication. The current authentication methods require significant resources and are vulnerable to specific attacks. The Non-Interactive Zero Knowledge Proof (NIZKP) concept suits IoT device authentication. Elliptic Curve Digital Signature (ECDSA) is a popular algorithm for IoT device authentication based on elliptic curve cryptography (ECC) and NIZKP. However, an intelligent attacker who captures the ECDSA signatures generated by the same random number can recover the private key. This paper proposes a timestamp based approach to prevent signature reuse and fake signature generation in the ECDSA algorithm. The signature proof is generated differently at each occurrence to prevent the generation of valid signatures from leaked private keys. The proposed approach eliminates the expensive modular inversion operations in signature generation and verification phases, enhancing execution efficiency. The analysis results indicate that the proposed Temporal ECDSA algorithm effectively prevents most attacks on client authentication in IoT networks. Various metrics, including computational complexity and security measures, were used to evaluate the effectiveness of the proposed approach, demonstrating that it outperforms most ECDSA variants.

Immobilization of lanthanide-doped strontium aluminate into electrospun nanofibrous film of poly(vinyl chloride) and cellulose acetate hybrid towards smart authentication

Photochromic inks have proven an appealing authentication approach to boost the anti-counterfeiting efficiency of commercial products. However, photochromic inks have considerable drawbacks such as poor durability and high cost. Herein, we develop a cutting-edge anti-counterfeiting material using new photochromic nanofibers. Electrospun poly(vinyl chloride)/cellulose acetate (PVCA) hybrid nanofibrous (80–320 nm) films were used to encapsulate nanoparticles of lanthanide-doped strontium aluminate (NLSA; 4-14 nm). Durability and photostability were guaranteed by the current authentication nanofibrous films encapsulated with an inorganic photochromic agent. NLSA-encapsulated PVCA nanofibers (NLSA@PVCA) showed excellent reversibility and photostability. When varying the concentrations of NLSA, a wide variety of photochromic and afterglow PVCA nanofibers were created with distinct emission properties. Under normal lighting conditions, NLSA@PVCA was invisible, but under ultraviolet (UV) illumination, a green emission was detected. The photochromic fibrous films were analyzed in a number of analytical techniques to determine their structure and chemical composition. Paper coated with NLSA@PVCA was studied to learn more about its mechanical properties. The excitation wavelength was reported to be 365 nm, indicating transparency, while the emission wavelength was reported to be 520 nm, designating a green color. Anti-counterfeiting of commercialized goods can be accomplished with the help of the current PVCA nanofibrous film.

A Lightweight Seamless Unimodal Biometric Authentication System

Nowadays, authentication remains a crucial and primordial process in securing Information Technology (IT) systems and infrastructures, while keeping users’ data more secure and privacy-preserving. Indeed, finding a distinctive characteristic(s) for each individual is done through personal identification and verification. Therefore, techniques such as Two-Factor Authentication (2FA) have been widely used to increase the security level in the authentication processes; where passwords are combined with secret codes or Unimodal Biometrics (UB). In this research study, we consider the case of 2FA based on UB, where the users are mainly asked each time to choose the suited biometric trait for their current authentication process. The thing that can obviously lead to a degradation of the User Experience (UX) and increases the Authentication Time (AT). To overcome these shortcomings, we propose in this article an “Autonomous Unimodal Biometric Authentication System”, abbreviated to “AUBAS”, which is modeled and developed on the basis of discrete-time Markov chains. It is a lightweight and seamless system that allows authenticating users according to two strategies, namely “likelihood” and “weighting”. Eventually, we take a case study and evaluate the system performance with the necessary simulations. Consequently, the implementation of AUBAS allows for improving the UX of an IT system and can be further applied in any environment or process relying on unimodal biometric authentication, such as cloud or mobile computing.

A robust brain pattern for brain-based authentication methods using deep breath

Security authentication involves the process of verifying a person's identity. Authentication technology has played a crucial role in data security for many years. However, existing typical biometric authentication technologies exhibit limitations related to usability, time efficiency, and notably, the long-term viability of the method. Recent technological advancements have led to the development of specific devices capable of reproducing human biometrics due to their visibility and tactile nature. Consequently, there is a demand for a new biometric method to address the limitations of current authentication systems. Human brain signals have been utilized in various Brain-Computer Interface (BCI) applications. Nevertheless, this approach also faces challenges related to usability, time efficiency, and most importantly, the stability of the method over time. Studies reveal that the stability of brain patterns poses a significant challenge in EEG-based authentication techniques. Stability refers to the capacity to withstand changes or disruptions, while permanency implies a lasting and unchanging state. Notably, stability can be temporary and subject to fluctuations, whereas permanency suggests a more enduring condition. Research demonstrates that utilizing alpha brainwaves is a superior option for authentication compared to other brainwave types. Many brain states lack stability in different situations. Interestingly, deep breathing can enhance alpha waves irrespective of the brain's current state. To explore the potential of utilizing deep breathing as a security pattern for authentication purposes, an experiment was conducted to investigate its effects on brain activity and its role in enhancing alpha brainwaves. By focusing on bolstering the permanency of brain patterns, our aim is to address the challenges associated with stability in EEG-based authentication techniques. The experimental results exhibited a high success rate of 91 % and 90 % for Support Vector Machine and Neural Network classifiers, respectively. These results suggest that deep breathing not only enhances permanency but could also serve as a suitable option for a brainwave-based authentication method.

A Hierarchical Authentication System for Access Equipment in Internet of Things

With the development of Internet of Things (IoT) technology, massive heterogeneous equipment involving all walks of life has been connected to networks. However, many current authentication methods have poor robustness and cannot ensure the safety of access equipment due to the complexity and uncertainty of the network environment. To deal with this problem, a hierarchical authentication system was proposed in this paper. First, an equipment identification method (EIM), using stack denoising autoencoders (SDAs), is developed to weaken the uncertainty to recognize the type of access equipment. Second, an equipment authentication method (EAM), based on the identification result and similarity theory, is designed to improve the verification accuracy to guarantee the credibility of access equipment. Third, the proposed hierarchical authentication system was tested in a real access platform. The experimental results demonstrated the satisfactory performance of this proposed hierarchical authentication system.

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System.

Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.

ICPDS: Design and Analysis of PSSOA and Blockchain Fusion Scheme for Cross-Hospital Medical Systems

<p>With the rapid development of information technology, medical treatments are gradually transforming into digitalization and informatization. The diverse sources and scopes of the medical data are sensitive and significant. As important data assets, medical data are constantly being analyzed and mined. Because of the huge commercial value of medical data, the problems of privacy leakage exist in the centralized information system. They cannot adapt to complex medical environment. Most of the current authentication mechanisms use passwords for verification. It is not easy for users to remember all usernames and passwords. Based on these problems, we design single-sign-on (called SSO) authentication (Password Single-Sign-On Authentication scheme, PSSOA) and blockchain (Improved Cross-Blockchain based Privacy-Preserving Date Sharing scheme, ICPDS) fusion schemes. Users are verified by multiple identity servers in this scheme. Then identity servers issue authentication tokens with threshold manners. The protected identity servers regularly update their master secret shares to defend against malicious adversaries.</p> <p> </p>

Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories

South African legislation regulates the classification/labelling and compositional specifications of raw beef patties, to combat processed meat fraud and to protect the consumer. A near-infrared hyperspectral imaging (NIR-HSI) system was investigated as an alternative authentication technique to the current destructive, time-consuming, labour-intensive and expensive methods. Eight hundred beef patties (ca. 100 g) were made and analysed to assess the potential of NIR-HSI to distinguish between the four patty categories (200 patties per category): premium 'ground patty'; regular 'burger patty'; 'value-burger/patty' and the 'econo-burger'/'budget'. Hyperspectral images were acquired with a HySpex SWIR-384 (short-wave infrared) imaging system using the Breeze® acquisition software, in the wavelength range of 952-2517 nm, after which the data was analysed using image analysis, multivariate techniques and machine learning algorithms. It was possible to distinguish between the four patty categories with accuracies ≥97%, indicating that NIR-HSI offers an accurate and reliable solution for the rapid identification and authentication of processed beef patties. Furthermore, this study has the potential of providing an alternative to the current authentication methods, thus contributing to the authenticity and fair-trade of processed meat products locally and internationally.

Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network

Cell-Free mMIMO is a part of technology that will be integrated with future 6G ultra-dense cellular networks to ensure unlimited wireless connectivity and ubiquitous latency-sensitive services. Cell-Free gained researchers’ interest as it offers ubiquitous communication with large bandwidth, high throughput, high data transmission, and greater signal gain. Cell-Free eliminates the idea of cell boundary in cellular communication that reduces frequent handover and inter-cell interference issues. However, the effectiveness of the current authentication protocol could become a serious issue due to the dynamic nature of Cell-Free in densely distributed, high number of users, high mobility, and frequent data exchange. Secondly, secure communication may be achieved in such a dynamic environment at the expense of high authentication overhead, high communication and computational costs. To address the above security challenges, we proposed a lightweight multifactor mutual authentication protocol for Cell-Free communication using ECC-based Deffie Hellman (ECDH). This scheme utilizes timestamping, one-way hash function, Blind-Fold Challenge scheme with public key infrastructure. The proposed cryptosystem integrates with blockchain technology using proof of staked (POS) as a consensus mechanism to ensure integrity, non-repudiation and traceability. The proposed scheme can enforce the mitigation of several major security attacks on communication links such as spoofing attacks, eavesdropping, user location privacy issues, replay attacks, denial of service attacks, and man-in-the-middle (MITM) attacks, which is one of the significant features of the scheme. Furthermore, this scheme contributes to reducing authentication, communication, and computational overheads with an average of 32.8%, 52.4% and 53.2% better performance respectively as compared baseline authentication protocols.

Secure healthcare system with insurance processing using blockchain

Electronic health records provide details about the patient's medications and medical history records. Health information draws attackers' attention since it holds important records. The delivery of the wrong medication or operation is the outcome of the loss of electronic health records. Less security measures are provided by healthcare systems for patient safety information. With the support of particular hospitals, traditional digital health records (EHRs) manage medical information one patient record at a time, which leads to the uncomfortable exchange of records. Cloud-based EHRs are able to share information more easily than traditional EHRs. For cloud-based EHRs, however, a cloud service centre and key generation centre present a specific problem. The proposed effort focuses on developing a new EHR paradigm that can address the centralized issue with cloud-based EHRs. Applying emerging block chain technologies to EHRs is the solution (denoted as block chain-based EHRs for convenience). First, in a block chain scenario, specify the system paradigm of block chain-based EHRs. Additionally, the authentication problem can be crucial for EHRs. On the other hand, the present authentication procedures for block chain-based EHRs have security issues of their own. Additionally, a suggestion for a block chain-based EHR authentication technique is presented here. Our remedy is a collusion-resistant role-based signature system with many signatories that can fend off an attack. Additionally, the suggested method is presumably secure and offers more effective signature and verification processes than current authentication systems in the paradigm of random oracles. The recommended study also focuses on how patients file insurance claims. It helps people get insurance from the authorized insurance sector.

SUACC-IoT: secure unified authentication and access control system based on capability for IoT

With the widespread use of Internet of Things (IoT) in various applications and several security vulnerabilities reported in them, the security requirements have become an integral part of an IoT system. Authentication and access control are the two principal security requirements for ensuring authorized and restricted accesses to limited and essential resources in IoT. The built-in authentication mechanism in IoT devices is not reliable, because several security vulnerabilities are revealed in the firmware implementation of authentication protocols in IoT. On the other hand, the current authentication approaches for IoT that are not firmware are vulnerable to some security attacks prevalent in IoT. Moreover, the recent access control approaches for IoT have limitations in context-awareness, scalability, interoperability, and security. To mitigate these limitations, there is a need for a robust authentication and access control system to safeguard the rapidly growing number of IoT devices. Consequently, in this paper, we propose a new secure unified authentication and access control system for IoT, called SUACC-IoT. The proposed system is based around the notion of capability, where a capability is considered as a token containing the access rights for authorized entities in the network. In the proposed system, the capability token is used to ensure authorized and controlled access to limited resources in IoT. The system uses only lightweight Elliptic Curve Diffie-Hellman Ephemeral (ECDHE), symmetric key encryption/decryption, message authentication code and cryptographic hash primitives. SUACC-IoT is proved to be secure against probabilistic polynomial-time adversaries and various attacks prevalent in IoT. The experimental results demonstrate that the proposed protocol’s maximum CPU usage is 29.35%, maximum memory usage is 2.79% and computational overhead is 744.5 ms which are quite acceptable. Additionally, in SUACC-IoT, a reasonable communication cost of 872 bits is incurred for the longest message exchanged.

Current authentication methods of herbs and herbal products: a systematic review

The authentication of herbal species is of great concern for the quality control of herbal products. Hence, many authentication methods have been developed for the accurate identification of herbal species. This systematic review aimed to gather information and provide evidence about the current authentication methods for several herbs and herbal products. In this study, the methodological quality was also evaluated using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement checklist method. As a result, a total of seventeen articles were found to suit the inclusion criteria of this study, which include molecular-based analysis, chemical fingerprinting, as well as macroscopic and analogical analysis. All of these methods have different approaches to the authentication of herbal-related products, and it also depends on the specific needs. This review also asserts that the combination methods might be a useful alternative for authentication purposes that produce better results. The multivariate analysis combined with analytical methods could be a great combination due to its great performance in authentication and suitability for routine screening.

Draw-a-Deep Pattern: Drawing Pattern-Based Smartphone User Authentication Based on Temporal Convolutional Neural Network

Present-day smartphones provide various conveniences, owing to high-end hardware specifications and advanced network technology. Consequently, people rely heavily on smartphones for a myriad of daily-life tasks, such as work scheduling, financial transactions, and social networking, which require a strong and robust user authentication mechanism to protect personal data and privacy. In this study, we propose draw-a-deep-pattern (DDP)—a deep learning-based end-to-end smartphone user authentication method using sequential data obtained from drawing a character or freestyle pattern on the smartphone touchscreen. In our model, a recurrent neural network (RNN) and a temporal convolution neural network (TCN), both of which are specialized in sequential data processing, are employed. The main advantages of the proposed DDP are (1) it is robust to the threats to which current authentication systems are vulnerable, e.g., shoulder surfing attack and smudge attack, and (2) it requires few parameters for training; therefore, the model can be consistently updated in real-time, whenever new training data are available. To verify the performance of the DDP model, we collected data from 40 participants in one of the most unfavorable environments possible, wherein all potential intruders know how the authorized users draw the characters or symbols (shape, direction, stroke, etc.) of the drawing pattern used for authentication. Of the two proposed DDP models, the TCN-based model yielded excellent authentication performance with average values of 0.99%, 1.41%, and 1.23% in terms of AUROC, FAR, and FRR, respectively. Furthermore, this model exhibited improved authentication performance and higher computational efficiency than the RNN-based model in most cases. To contribute to the research/industrial communities, we made our dataset publicly available, thereby allowing anyone studying or developing a behavioral biometric-based user authentication system to use our data without any restrictions.

A Multi-Tier MQTT Architecture with Multiple Brokers Based on Fog Computing for Securing Industrial IoT

With the rapid growth of internet-connected devices and their resource-constrained capabilities, the current authentication mechanisms are unable to meet the complex IoT application requirements, such as in the Industrial Internet of Things (IIoT), due to the increased computation, communication, and storage overhead arising from these mechanisms. In the IIoT, machine-to-machine (M2M) communication is an underlying technology where devices (e.g., sensors, actuators, and controllers) can be enabled to exchange information autonomously; thus, the massive data generated by these devices can increase latency, network congestion, and the complexity of security management. Message queue telemetry transport (MQTT) is one of the promising M2M protocols used in the IoT that could encounter such issues because it relies on a central broker in the cloud and implements a heavyweight authentication mechanism based on TLS. Therefore, this paper proposes an MQTT architecture with multi-tier brokers based on fog computing, where each broker is deployed with an authentication manager. In addition, the paper presents a lightweight mutual authentication scheme based on hash function and XOR operation. Comparing the results given in the benchmark, the overall performance of our scheme shows that storage and communication overheads are reduced to 89% and 23%, respectively. Furthermore, our system can resist against several cyberattacks and provide scalability.

Secure Protocol for Resource-Constrained IoT Device Authentication

Wireless sensor networks (WSNs) are crucial components of internet of things (IoT) and have been deployed in numerous fields such as battlefield surveillance. The exploitation of broadcasts in WSNs renders these networks susceptible to numerous attacks. Consequently, to boost security, reliability, and successful cooperation, trust must be established among the sensor nodes. Unfortunately, the current authentication and authorization approaches exhibit high key management overheads, depend on static digital signatures or trusted third parties, and have both high communication latencies and computational complexity that render them inefficient. In this paper, challenge-response mutual authentication protocol is proposed for enhancing security in WSN-based IoT environment. The simulation results showed that the proposed protocol has the least transaction costs, time complexity, end to end delays and energy consumptions. It is also resilient against dictionary, side channel, cloning, man-in-the-middle (MitM, denial of service (DoS) and next password prediction attacks.