Message Authentication Research Articles

Improved RSA dynamic cryptographic accumulator-based anonymous batch authentication scheme for Internet of Vehicles

The Internet of Vehicles (IoV) provides various services to vehicles through information sharing to ensure road safety and improve traffic efficiency. However, malicious attackers can challenge the privacy and security systems of the IoV by conducting security attacks. Currently, many schemes use bilinear pairing or elliptic curves to construct authentication systems, ensuring anonymity of vehicle identities and message security. However, these schemes suffer drawbacks such as low computational efficiency and high communication overhead in highly dynamic IoV. Therefore, we propose a new efficient certificateless message authentication scheme. The proposed scheme constructs a lightweight security authentication protocol by improving the RSA dynamic accumulator and combining it with non-interactive discrete logarithm zero-knowledge proofs. In addition, our scheme eliminates the need for bilinear pairing operations, thereby reducing computational overhead. Simultaneously, it enables real-time tracking and revocation of malicious vehicle identities. Security analysis shows that our scheme can be reduced to a secure S-RSA assumption under the random oracle model, meeting various security requirements of the IoV. Performance analysis shows that our scheme diminishes not only the computational overhead but also the communication overhead.

Advancing data honesty in experimental biology.

The ease with which scientific data, particularly certain types of raw data in experimental biology, can be fabricated without trace begs urgent attention. This is thought to be a widespread problem across the academic world, where published results are the major currency, incentivizing publication of (usually positive) results at the cost of lax scientific rigor and even fraudulent data. Although solutions to improve data sharing and methodological transparency are increasingly being implemented, the inability to detect dishonesty within raw data remains an inherent flaw in the way in which we judge research. We therefore propose that one solution would be the development of a non-modifiable raw data format that could be published alongside scientific results; a format that would enable data authentication from the earliest stages of experimental data collection. A further extension of this tool could allow changes to the initial original version to be tracked, so every reviewer and reader could follow the logical footsteps of the author and detect unintentional errors or intentional manipulations of the data. Were such a tool to be developed, we would not advocate its use as a prerequisite for journal submission; rather, we envisage that authors would be given the option to provide such authentication. Only authors who did not manipulate or fabricate their data can provide the original data without risking discovery, so the mere choice to do so already increases their credibility (much like 'honest signaling' in animals). We strongly believe that such a tool would enhance data honesty and encourage more reliable science.

Some methodological issues in language research: Dealing with transcribed interpreted courtroom data

Transcription is a crucial tool in language research, particularly in discourse analysis, as it provides a distillation of real-time interactions. In the 21st century, researchers are increasingly interested in studying authentic data samples, such as audio-recorded court hearings, to turn evanescent speech into readable and analysable formats. However, transcription involves a complex process of theoretical or methodological issues about language, making it a rich source of examinable data. Researchers need to develop adequate methodologies to make such data available for their research endeavours. This exploratory research presents transcription as a methodology for researchers interested in language and ethnographic methods, addressing critical considerations such as the data to be transcribed, the transcriber responsible, and how to represent it. The paper explores innovations in transcription and presents the benefits and challenges of transcription as a methodology, particularly in language research.

“Graphical Password Authentication using Blockchain”

Abstract: This paper proposes a novel approach to authentication by integrating graphical passwords with blockchain technology. By storing authentication data on the blockchain, we aim to enhance security and accountability. Our system offers tamper-proof records and decentralized authentication, contributing to improved security in digital systems.

Research on Fatigue Driving Detection Technology Based on CA-ACGAN.

Driver fatigue represents a significant peril to global traffic safety, necessitating the advancement of potent fatigue monitoring methodologies to bolster road safety. This research introduces a conditional generative adversarial network with a classification head that integrates convolutional and attention mechanisms (CA-ACGAN) designed for the precise identification of fatigue driving states through the analysis of electroencephalography (EEG) signals. First, this study constructed a 4D feature data model capable of mirroring drivers' fatigue state, meticulously analyzing the EEG signals' frequency, spatial, and temporal dimensions. Following this, we present the CA-ACGAN framework, a novel integration of attention schemes, the bottleneck residual block, and the Transformer element. This integration was designed to refine the processing of EEG signals significantly. In utilizing a conditional generative adversarial network equipped with a classification header, the framework aims to distinguish fatigue states effectively. Moreover, it addresses the scarcity of authentic data through the generation of superior-quality synthetic data. Empirical outcomes illustrate that the CA-ACGAN model surpasses various extant methods in the fatigue detection endeavor on the SEED-VIG public dataset. Moreover, juxtaposed with leading-edge GAN models, our model exhibits an efficacy in in producing high-quality data that is clearly superior. This investigation confirms the CA-ACGAN model's utility in fatigue driving identification and suggests fresh perspectives for deep learning applications in time series data generation and processing.

RETRACTED: Ahmad et al. Neuroprotection against Aluminum Chloride-Induced Hippocampus Damage in Albino Wistar Rats by Leucophyllum frutescens (Berl.) I.M. Johnst. Leaf Extracts: A Detailed Insight into Phytochemical Analysis and Antioxidant and Enzyme Inhibition Assays. Front. Biosci. (Landmark Ed) 2023, 28(8), 184.

The Editors-in-Chief have retracted the article titled "[Neuroprotection against Aluminum Chloride-Induced Hippocampus Damage in Albino Wistar Rats by Leucophyllum frutescens (Berl.) I.M. Johnst. Leaf Extracts: A Detailed Insight into Phytochemical Analysis and Antioxidant and Enzyme Inhibition Assays]" ([1]) due to significant concerns regarding the reliability and integrity of the data presented. After the publication of the article, several issues were brought to our attention regarding the originality and authenticity of the visual data within the manuscript. Specifically, Figure 4 of the article contains images that are identical to those in the previously published papers [2, 3]. This duplication of images raises serious questions about the validity of the results and the adherence to ethical standards of research. Despite multiple attempts to contact the authors for an explanation and an opportunity to address these concerns, no satisfactory response was provided. Given the lack of accountability and the serious nature of the academic misconduct implied, the Editor-in-Chief, after careful consideration and in accordance with the publication's ethical guidelines, has decided to retract the article.

HSM4SSL: Leveraging HSMs for Enhanced Intra-Domain Security

In a world where digitization is rapidly advancing, the security and privacy of intra-domain communication within organizations are of critical concern. The imperative to secure communication channels among physical systems has led to the deployment of various security approaches aimed at fortifying networking protocols. However, these approaches have typically been designed to secure protocols individually, lacking a holistic perspective on the broader challenge of intra-domain communication security. This omission raises fundamental concerns about the safety and integrity of intra-domain environments, where all communication occurs within a single domain. As a result, this paper introduces HSM4SSL, a comprehensive solution designed to address the evolving challenges of secure data transmission in intra-domain environments. By leveraging hardware security modules (HSMs), HSM4SSL aims to utilize the Secure Socket Layer (SSL) protocol within intra-domain environments to ensure data confidentiality, authentication, and integrity. In addition, solutions proposed by academic researchers and in the industry have not addressed the issue in a holistic and integrative manner, as they only apply to specific types of environments or servers and do not utilize all cryptographic operations for robust security. Thus, HSM4SSL bridges this gap by offering a unified and comprehensive solution that includes certificate management, key management practices, and various security services. HSM4SSL comprises three layers to provide a standardized interaction between software applications and HSMs. A performance evaluation was conducted comparing HSM4SSL with a benchmark tool for cryptographic operations. The results indicate that HSM4SSL achieved 33% higher requests per second (RPS) compared to OpenSSL, along with a 13% lower latency rate. Additionally, HSM4SSL efficiently utilizes CPU and network resources, outperforming OpenSSL in various aspects. These findings highlight the effectiveness and reliability of HSM4SSL in providing secure communication within intra-domain environments, thus addressing the pressing need for enhanced security mechanisms.

Getting better? Examining the effects of social support in OHCs on users’ emotional improvement

Online health communities (OHCs) have emerged as dynamic platforms facilitating patient communication and knowledge exchange on health matters. While extant research underscores the emotional benefits accrued from engagement in OHCs, the precise quantification of how social support within these communities influences users’ emotional states remains an intricate challenge. In light of this, the primary objective of this study is to explore the influential mechanisms underlying the received social support on the emotional improvement of users in OHCs, while concurrently investigating the potential moderating effects posed by individuals’ health conditions and levels of community involvement. Leveraging authentic user interaction data harvested from an online diabetes community, this study employed deep learning models and natural language processing methods to gauge the magnitude of received social support and the variation of users’ emotional states. Subsequently, a multiple linear regression model was formulated to evaluate the effects of both informational and emotional support on users' emotional improvement. Empirical results reaffirm the pivotal role of both informational and emotional support in enhancing users’ emotional states, with emotional support emerging as particularly influential. Notably, prolonged disease duration amplifies the beneficent impact of social support on emotional enhancement. The research outcomes bear implications for ameliorating negative emotional experiences and effectively managing chronic diseases.

The Paradox of Power: Securing User Data in the Cloud's Shadow

Whilst the blessing of the cloud, which provides for adaptability and easy data sharing as well as storage, comes with some security doubts. We place a major responsibility on our backs of protecting the data provided by our users from any evil attempts to sneak in and also risks of being accidentally exposed and the ones that exist through shared infrastructure. This paper traverses this complex terrain by noting the need for targeted security solutions which are aimed towards dealing with the compounded nature of the fast-growing problem. By exploring such threats as data breaches, the possible encryption breaks and the nature of server sharing we will analyze the safety of cloud-based services. We position that the existing security methods, though very necessary, are not exclusive in responding to the ones posed by the new threats. This means we will therefore be rather flexible in our design of the data security in the cloud. By the way, we will employ innovative strategies, maybe, by replacing a word for words such as homomorphic encryption, zero-knowledge proofs, and federated learning, presenting how it holds promise for private and confidential assets. Furthermore, we investigate the exploding influence of blockchain technology, regarding the wages it might deserve in providing manipulated data authentication and creating trust. The work in this paper creates a path towards a future when database users' virtual information in the cloud is safe and certified. Through proposing a wide-ranged approach, which equates realized ideas with well-established security frameworks, we shall lead a cloud infrastructure where stability and power would prevail. Keywords – Cloud Computing, Security Issues, Security Challenges

A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning

The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.

Enhancing Resilience in Biometric Research: Generation of 3D Synthetic Face Data Using Advanced 3D Character Creation Techniques from High-Fidelity Video Games and Animation.

Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the resulting unbalanced representation and bias, as well as the limited availability of diverse datasets for the development and evaluation of biometric systems, has emerged. Methods for a parameterized generation of highly realistic synthetic data are emerging and the necessary quality metrics to prove that synthetic data can compare to real data are open research tasks. The generation of 3D synthetic face data using game engines' capabilities of generating varied realistic virtual characters is explored as a possible alternative for generating synthetic face data while maintaining reproducibility and ground truth, as opposed to other creation methods. While synthetic data offer several benefits, including improved resilience against data privacy concerns, the limitations and challenges associated with their usage are addressed. Our work shows concurrent behavior in comparing semi-synthetic data as a digital representation of a real identity with their real datasets. Despite slight asymmetrical performance in comparison with a larger database of real samples, a promising performance in face data authentication is shown, which lays the foundation for further investigations with digital avatars and the creation and analysis of fully synthetic data. Future directions for improving synthetic biometric data generation and their impact on advancing biometrics research are discussed.

A secure authentication and collaborative data-sharing model based on a blockchain network in the cloud

ABSTRACT In cloud computing, various organisations store their confidential information in private or public cloud platforms. The data sharing through the various cloud platforms increases the risk of security. To attain data sharing while sustaining data confidentiality, a secure sharing platform and collaboration model is needed. Hence, this paper introduces the novel secure authentication and collaborative data sharing (SecAuth_CDS) scheme for sharing data on blockchain. The entities involved in the SecAuth_CDS are third parties, data owners, smart contracts and blockchain networks. The security of the authentication process can be boosted by applying various security operations, such as Exclusive OR (EXOR), hashing, encryption and so on. The proposed method assures that the sharing is secure and trustworthy by blockchain. The experimental result reveals that the SecAuth_CDS performed effectively in terms of revenue, memory usage and computation cost with the values of 27, 62.20 MB and 0.003 sec.

Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Beyond Likes, Comments, and Shares: How Can Influencer Marketing Drive Customer Equity and Purchase Intention?

While researchers have shown notable interest in influencer marketing for promoting customer-brand relationships, its impact on customer equity and, in turn, purchase intention warrants further investigation. This study explores how influencers’ quantitative metrics—including the number of “likes,” “comments,” and “shares”—displayed on their content affect purchase intentions through perceived message authenticity and customer equity. The data were gathered from 432 Facebook customers and analyzed by partial least squares structural equation modeling (PLS-SEM). The findings revealed that the number of “likes,” “shares,” and “comments” directly influences perceived message authenticity and indirectly influences customer equity drivers (including value equity, brand equity, and relationship equity) through perceived message authenticity. Customer equity drivers significantly influence purchase intention and mediate the effect of perceived message authenticity on purchase intention. This study is one of the few that focuses on demonstrating the relationship process through which influencers’ interactive content shapes customer equity and purchase intention. It extends the influencer marketing literature by adding new empirical evidence regarding the significant role of influencer marketing in maintaining customer-brand relationships, which receives a wide interest from researchers, practitioners, and social media marketers.

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

Heart failure remote monitoring: novel approaches and management strategies

Background. Globally, heart failure is a leading health problem, with an estimated 64 million cases worldwide, including 6.7 million in the U.S., according to estimates. The U.S. economic burden is expected to see a steep rise by the year 2030. Heart failure is a cause of 8.5% of heart disease-related deaths and a central cardiovascular killer. Emergency hospitalization rates and readmission rates are high. Methods: A systematic methodology was followed to generate authentic and reliable data on remote monitoring in the setting of heart failure patients. The inclusion criteria comprise articles describing remote monitoring interventions published in peer-reviewed journals and carried out in human subjects in English. Critical analysis applies quality assessment tools to assess methodological soundness, possible bias, and relevance to the research objective. Results: This review discusses wearable devices, e.g., Zoll HFMS ReDS, and Audicor, each effectively monitoring cardiac parameters and reducing H.F. hospitalizations. Implantable cardiac monitors such as LUX-Dx and CardioMEMS H.F. RM has potential to give real-time data for timely intervention and tailored therapies. The integration of machine learning algorithms in devices, for example, VitalPatch and the SimpleSense has led to increased use of these devices to make precise and efficient health care predictions, leading to improved patient outcomes. Conclusion: From all the research, remote monitoring devices and strategies are recommendable for patients with various cardiac complications. It can improve heart function, however, R.M. has not been seen to reduce the overall mortality rate among heart patients.

Strategic team design for sustainable effectiveness: A data-driven analytical perspective and its implications

Teams are building blocks of organizations and essential inputs of organizational success. This article studies a data-driven analytical approach that exploits the rich data accumulated in organizations in the digital era to design teams, including prescribing team composition and formation decisions. We propose to evaluate a team regarding its performance and temporal stability, referred to as sustainable effectiveness (SE). Our approach estimates the team's performance and stability using machine learning models. It then optimizes an integrated objective of the team's performance and stability through mixed-integer programming models formulated according to predictive models. Consequently, this approach mines meaningful team compositions from historical data and guides strategic team formation accordingly. We conduct empirical studies using authentic data from our partner company in the real estate brokerage industry. The findings reveal that teams that adhere to our model's recommendations achieve an average percentage improvement of 153.1% to 156.5% higher than the benchmark teams, particularly when recruiting one or two members in their actual SE during the post-formation period. We further disclose the mechanism underlying this improvement from the perspective of changes in team compositions. Our study provides a decision support tool for team design and ensuing team dynamic management.

Revolutionizing healthcare 5.0: Blockchain-driven optimization of drone-to-everything communication using 5G network for enhanced medical services

The healthcare industry has witnessed a notable surge in the adoption of drone technology for the swift and efficient transportation of vital medical supplies. In the expansive and uncontrolled realm where drones operate, challenges arise concerning authentication and secure data sharing. In the era of Healthcare 5.0, characterized by the integration of cutting-edge technologies, this article explores a groundbreaking approach to revolutionize medical services through the synergy of blockchain technology, drone-to-everything (D2X) communication, and the upcoming 5G network that provides enhanced broadband, extra reliable, low latency communication and massive machine type communication. This research addresses the critical task of delivering medical supplies using drone technology, focusing on ensuring the security, reliability and demand for a large storage capacity during the transportation of sensitive items.This research introduces an optimized communication link by using a blockchain network and the Internet of Things (IoT). To achieve such a goal two different algorithms are introduced and three different scenarios are implemented to include all possibilities in real applications. Three potential routes for data transmission, drone to drone, drone to ground user devices, and drone to base station, are explored to enhance communication efficiency. The contribution lies in optimizing the communication link to ensure a low bit error rate (BER), high signal-to-interference noise ratio (SINR) and maintain fixed signal power during data transmission. The outcomes of this study have significant implications for advancing healthcare logistics through the integration of cutting-edge technologies. The performance of the proposed method has been proven by simulations using parametric measurements.

A Sustainable Supply Chain Model with Low Carbon Emissions for Deteriorating Imperfect-Quality Items under Learning Fuzzy Theory

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller’s responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain.

Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time.