Means Of Authentication Research Articles

Simply tell me how---On Trustworthiness and Technology Acceptance of Attribute-Based Credentials

Attribute-based Credential Schemes (ACS) have been long proposed as privacy-preserving means of authentication, yet have not found wide-spread adoption to date. We ask what factors explain whether users will adopt ACS or not. To that end, we aim to comprehend how factors interrelate to predict ACS technology acceptance and to investigate how intrinsic and presentation aspects of the ACS cause the intent to use them. We conducted two between-subject, random-controlled trials with a combined UK-representative sample of N = 812 participants. After having stated their privacy concerns and faith-in-technology, each participant then inspected one variant of an ACS website, which encoded a combination of three intrinsic aspects and three presentation aspects of an ACS. Participants then reported on the perceived trustworthiness, perceived usefulness, and their behavioral intention to adopt the technology. We proposed an extended Technology Acceptance Model incorporating privacy concern and perceived trustworthiness and show in covariance-based structural equation modeling that the model explains user decision making very well. We could show that communicating facilitating conditions and demonstrating results drives the overall technology acceptance. Communicating with simple language impacted the behavioral intent to use the ACS positively. Our work is the first to show cause-effect relations for ACS adoption with substantial sample size. This study not only informs what factors impact the technology acceptance of ACS, but likely also translates to other privacy-enhancing technologies and yields methodological considerations for research into the privacy paradox at large.

The Future Risk of Biometric Data Theft in Cybersecurity

The aim of this paper is to analyze the technical and engineering issues surrounding the future risk of biometric data theft in cyber security. Biometric data, such as fingerprints, facial recognition, and voice recognition, are increasingly being used as a means of authentication in various industries. However, the collection, storage, and transmission of biometric data present unique security challenges, as this data is highly personal and cannot be changed if compromised. In this paper, we will examine the vulnerabilities in biometric authentication technologies, the risks associated with the use of biometrics in various industries, and the measures that can be taken to mitigate these risks.

Prinsip Jaminan Hukum Sebagai Jaminan Sertifikasi Tanda Tangan Elektronik

Technological developments by international organizations see the need for recognition and regulation of legal certainty in the field of information technology, regarding electronic transactions with digital signatures. Legal certainty regulates clearly and logically, which means there is clarity and firmness in the application of law to e-signature regulations, and e-signature certification to guarantee legal certainty. Regulations on the use of e-signatures are regulated in Law Number 19 of 2016 concerning Amendments to Law Number 11 of 2008 concerning Electronic Information and Transactions. Recognition of electronic signatures as valid legal evidence and electronic certification providers. The use of e-Signature is also regulated in implementing laws and regulations, namely Government Regulation Number 71 of 2019 concerning the Implementation of Electronic Systems and Transactions, including the introduction of electronic signatures as a legal instrument, electronic signatures as a means of authentication and verification, data for creating electronic signatures , electronic signature signing process, electronic certification organizer. To use electronic signatures, you need to choose a company that has received certification and can operate globally without having to worry about document security.

Surveying Emerging Trends in DDoS Defense

This The DDoS attack threat is evolving, and because of this, organizations are discovering and using new modern technologies to lay the ground for more effective defensive strategies. This paper is devoted to the investigation of the most efficient methods fighting DDoS – downtime of the network, and ensuring cybersecurity on different domains. First of all, the integration of Convolutional Neural Networks (CNNs) into cybersecurity is a very promising move with respect to fighting exactly the phishing and application-layer DDoS attacks in greater details than the machine learning approaches like the LSTMs and SAEs. Another aspect of building the effective opposition against the dummy data attacks on the critical infrastructures, for example on the power systems, is creating the multi-dimensional mitigation models composed of various timely detection techniques and robust network architecture. In addition, the usage of Physically Unclonable Functions (PUFs) in network architectures provides a means of authentication as well as access control that can improve the resilience of a network against DDoS attacks. PUFs enables the blockade of unwanted packets of high volume traffic, allowing granular traffic filtration and isolation. By using hardware solutions such as Distributed-Denial-of-Service (DDoS) attack prevention, SDN-biased security frame with deep learning algorithms can improve network resilience with significant detection and response to slow-rate DDoS attacks. At last EWMA, KNN, and CUSUM as statistical methods integrated with FOG computing architectures ensure real time and effective solution for the detection and mitigation of DDoS attacks in the IoT networks, making them immune to the current as well as the continuously emerging cyber threats. Through the integration of these cutting edge methods, organizations will be able to hold their ground against cyberattacks catalyzed by DDoS menace and stay ahead of dynamic threats whenever they arise. Keywords— Cloud computing, Data threats, Data Protection, Cloud security.

Enhancing Security Within Restricted Hospital Zones by Integrating Finger Vein Authentication

The integration of finger vein authentication technology to enhance security measures within restricted zones in hospital environments, is the aim of this project. An advanced biometric technique called finger vein authentication uses each person's individual vein patterns to confirm their identification. It is especially desirable for applications where security is critical, such financial transactions, healthcare, and access control, because it provides a very safe and non-intrusive means of authentication. Although there has been considerable success with traditional finger vein authentication methods, deep learning techniques have completely changed this field. A branch of artificial intelligence called deep learning has shown impressive results in pattern recognition and picture analysis, which makes it a prime contender for improving the precision and dependability of finger vein authentication systems. Finger vein authentication utilizes biometric characteristics unique to everyone, providing a highly secure method of identity verification. By implementing this technology, hospitals can bolster access control, prevent unauthorized entry into sensitive areas, and safeguard patient information and critical resources. The project aims to implement finger vein authentication systems to regulate access to restricted areas such as operating rooms, medication storage areas, and patient information sections. Authorized personnel will undergo authentication via finger vein scans for entry, ensuring that only authorized individuals can access these zones.

Enhancing Signature Forgery Detection System Using CNN-SVM

Handwritten signatures are widely used as a means of personal identification and authentication. Many documents like bank cheques and legal transactions require signature verification. But considering a large number of documents, it is a very difficult and time-consuming task. Therefore, ensuring the necessity for a robust automatic signature verification tool that aims to reduce fraud in all related financial transaction sectors. The current visual verification depends mainly on the experience, mood, and working environment of the verifier which ultimately wastes both time and money. Moreover, it is difficult for the eyes of any experts to precisely verify the ratios between lines and angles of a genuine signature to a fraud signature. Therefore, we propose an automatic signature verification technique using the recent advances in image processing and machine learning.

Student Virtual Identity System

Abstract: In the ever-evolving landscape of modern education, the imperative of robust and secure systems for student identity management has taken center stage. Enter the Student Virtual Identity System (SVIS), a comprehensive solution meticulously crafted to meet this demand. SVIS is a versatile platform that seamlessly caters to the needs of students, educational institutions, and administrators, offering a secure and efficient means of managing, verifying, and harnessing digital student identities.At the heart of SVIS is a user- friendly registration process, ensuring an easy and secure onboarding experience for students. Upon enrollment, students are granted access to a virtual identity that opens the doors to a multitude of educational services, from course registration to library access and Institutional Efficiency and Security. Educational institutions stand to gain significantly from SVIS. Administrative processes related to student enrollment, and record-keeping are streamlined, reducing the burden on staff and enhancing operational efficiency. SVIS is a bulwark against identity fraud and unauthorized access, bolstering security and protecting educational resources. SVIS introduces a consistent, secure, and standardized means of authentication, reducing the risk of identity theft and thwarting potential cyberattacks. Its adaptability ensures compatibility with various educational technology platforms, learning management systems, and a wealth of online resources, making it a versatile solution for the modern educational landscape.In conclusion, the Student Virtual Identity System is not merely a tool for simplifying student identity management; it's a catalyst for an educational revolution. SVIS doesn't just meet the needs of the moment; it enhances the security, efficiency, and convenience of digital learning environments. Its implementation signifies a commitment to providing a more reliable and secure experience for students and educators in the dynamic and increasingly virtual education landscape. Welcome to a new era in education with SVIS!

Fuzzified Deep Learning based Forgery Detection of Signatures in the Healthcare Mission Records

In an era subjected to digital solutions, handwritten signatures continue playing a crucial role in identity verification and document authentication. These signatures, a form of bio-metric verification, are unique to every individual, serving as a primitive method for confirming identity and ensuring security of an individual. Signatures, apart from being a means of personal authentication, are often considered a cornerstone in the validation of critical documents and processes, especially within the healthcare sector. In healthcare missions, particularly in the regions that are underdeveloped, hand-written records persist as the primary mode of documentation. The credibility of these handwritten documents hinges on the authenticity of the accompanying signatures, making signature verification a paramount safeguard for the integrity and security of medical information. Nonetheless, traditional offline methods of signature identification can be time-consuming and inefficient, particularly while dealing with a massive volume of documents. This arises the evident need for automated signature verification systems. Our research introduces an innovative signature verification system which synthesizes the strengths of fuzzy logic and CNN (Convolutional Neural Networks) to deliver precise and efficient signature verification. Leveraging the capabilities of Fuzzy Logic for feature representation and CNNs for discriminative learning, our proposed hybrid model offers a compelling solution. Through rigorous training, spanning a mere 28 epochs, our hybrid model exhibits remarkable performance by attaining a training accuracy of 91.29% and a test accuracy of 88.47%, underscoring its robust generalization capacity. In an era of evolving security requirements and the persistent relevance of handwritten signatures, our research links the disparity between tradition and modernity.

Finger Vein Authentication using Neural Network

In today's rapidly evolving world, security has become a paramount concern. Biometric-based methods have emerged as a reliable and accurate means of authentication, with hand-based biometric traits being easily accessible for data collection. In this paper, we present a novel approach for authentication utilizing finger-vein images, addressing the challenges of collecting and processing biometric trait images for large organizations. However, the collection, storage, and processing of biometric trait images for a large number of employees pose significant challenges. To overcome these challenges, our proposed approach leverages deep learning techniques to authenticate individuals based on finger-vein images. The rise in technological advancements has brought about an elevated risk to both personal data and national security. Existing methods designed to safeguard crucial information from external threats were found to be insufficient. There emerged a necessity to implement more advanced technologies that could ensure a higher level of efficiency in protecting our data from unauthorized access. In this proposed method we use Sobel filter , open cv and gaussian blur for image pre-processing, sequential model with transfer learning for feature extraction and finally, Adam optimizer for decision making. The proposed model has shown 98% accuracy in authenticating finger veins. Keywords: Sobel Filter, Open CV, Sequential Model, Transfer Learning, Adam Optimiser.

Evaluation of the parameters of the simulation model of microcontroller authenticators

Microcontroller technologies are the basis for intelligent devices, so cybersecurity issues are important when ensuring the exchange of information between them. As these devices form a variety of intelligent systems, authentication becomes a key aspect of cybersecurity. This process distinguishes between legitimate and illegitimate devices, allowing legitimate devices to access system resources and reject requests from illegitimate ones. Today there is a problem of choosing an authentication factor for a large number of electronic devices of the same type in the information system. The purpose of this study is to find out the possibility of a programmatic approach to simulating a "biometric" authentication template. To solve the goal, it is necessary to implement a simulator of templates with the properties of biometrics in a programming language, to evaluate the distribution of distances between pairs of templates, and to evaluate the parameters of the generated sequences from the point of view of brute force probability. In this study, a solution to the problem is proposed, which combines the ideas of the biometric factor and the possibilities of fast software generation of authenticators for microcontrollers. The work is devoted to the evaluation of the model parameters of the simulator of dynamic bit patterns of authentication based on the generator of pseudo-random impulse Poisson sequences. The work highlights the basic principles of the simulator's functioning and the advantages of using dynamic templates to ensure cyber security of electronic devices built on the basis of microcontrollers. Estimates of the main parameters of the simulator model were performed. The results of the work are important for the development of security of electronic systems and means of authentication in the context of cybersecurity of microcontrollers.

WPA2 Security Enhancement Method through NFT in Web3 Era

WPA2 is the most used wireless communication protocol in the world (2023). It first appeared in 2006, and now several vulnerabilities have been identified. To use WPA2-EAP or WPA3 (2018), which were released to compensate for the vulnerabilities of WPA2, additional equipment upgrades are required for STA (station) and AP (access point, router), which are connecting devices. We are currently living in the Web3 era. In the future society, people will have more than one NFT each. It is possible to improve the security of WPA2 by using this as an authentication means. In this paper, see the principles of WPA2 crack tools that are currently used today and suggest a way to defend against them using NFT. An experiment was carried out on the security of WPA2, which is widely used in SOHO environments, using only SBC (Single Board Computer) and NFT without expensive routers or additional authentication means. Hacking time, Internet connection delay time, download speed, etc. were compared on various PCs. In conclusion, this proposal demonstrated that representative WPA2 cracking tools can be defended without performance degradation compared to existing WPA2.

A Review Study on various Image Security Techniques and Emerging trends for Visual Data Protection

In the rapidly evolving digital age, the proliferation of visual data and images has led to increased concerns about their security and protection. This paper reviews various image security techniques and emerging trends aimed at safeguarding visual data in a digital landscape. The research explores the evolution of image security measures and their adaptation to address contemporary challenges in data protection. This study introduces the stage by elucidating the multifaceted nature of image security, emphasizing its significance in preserving the confidentiality and integrity of visual data. The complex landscape of image security threats is examined, highlighting the challenges faced in safeguarding visual data from various vulnerabilities. This paper delves into the need and significance of image security in the digital age, presenting a compelling case for the adoption of robust security measures. It explores the risks associated with image theft and tampering in the online world and underscores the essential role of image encryption and authentication methods in countering these threats. The study then delves into the realm of encryption techniques, offering insights into both symmetric and asymmetric cryptographic algorithms designed to enhance image security on the internet. The research extends its focus to secure data hiding techniques, such as reversible data hiding in encrypted images, histogram shifting-based data hiding, and high-embedding capacity methods employing optimization algorithms. The paper also explores digital watermarking as a means of image authentication and copyright protection, along with image tamper detection and self-recovery mechanisms in watermarking schemes. Additionally, this review assesses the impact of emerging trends, including machine learning, Blockchain, and AI-based solutions, on the security of visual data, such as deep neural networks, generative adversarial neural networks, and their role in image steganography, forensics, and privacy protection. Consequently, by providing an in-depth analysis of the current state of image security and future directions, this study contributes to the understanding of the critical role played by image security in the digital age and its implications for data protection and privacy.

VibPath

Technical advances in the smart device market have fixated smartphones at the heart of our lives, warranting an ever more secure means of authentication. Although most smartphones have adopted biometrics-based authentication, after a couple of failed attempts, most users are given the option to quickly bypass the system with passcodes. To add a layer of security, two-factor authentication (2FA) has been implemented but has proven to be vulnerable to various attacks. In this paper, we introduce VibPath, a simultaneous 2FA scheme that can understand the user's hand neuromuscular system through touch behavior. VibPath captures the individual's vibration path responses between the hand and the wrist with the attention-based encoder-decoder network, authenticating the genuine users from the imposters unobtrusively. In a user study with 30 participants, VibPath achieved an average performance of 0.98 accuracy, 0.99 precision, 0.98 recall, 0.98 f1-score for user verification, and 94.3% accuracy for user identification across five passcodes. Furthermore, we also conducted several extensive studies, including in-the-wile, permanence, vulnerability, usability, and system overhead studies, to assess the practicability and viability of the VibPath from multiple aspects.

Detecting malicious reviews and users affecting social reviewing systems: A survey

The proliferation of attacks on On-line Social Networks (OSNs) has imposed particular attention by providers and users. This has an even higher importance for Social Reviewing Systems (SRSs), where users can be strongly conditioned by means of malevolent reviews and behavior of fake or camouflage accounts. There is a rich literature on the use of strong authentication means, encryption or privacy-preserving schemes for OSNs and SRSs, but these mechanisms only represent a first defense line. Advanced attacks may be able to bypass such a defense and to successfully threaten the system and harm its users. Therefore, it is needed to embed a second defence line to peculiar attack scenarios of SRSs that take advantage of user dynamics occurring within social networks. This survey focuses on the issue and solutions to detect malicious reviews and users so as to exclude them from social networks, protect legitimate and honest users and keep the credibility of the protected SRS as high as possible. Therefore, we provide an evaluation of the main detection solutions for the mentioned specific attacks against SRSs in according to different metrics on several standard datasets.

Radicalization of Airspace Security: Prospects and Botheration of Drone Defense System Technology

The development of a comprehensive and decisive drone defense integrated control system that can provide maximum security is crucial for maintaining territorial integrity and accelerating smart aerial mobility to sustain the emerging drone transportation system (DTS) for priority-based logistics and mobile communication. This study explores recent developments in the design of robust drone defense control systems that can observe and respond not only to drone attacks inside and outside a facility but also to equipment data such as CCTV security control on the ground and security sensors in the facility at a glance. Also, it considered DDS strategies, schema, and innovative security setups in different regions. Finally, open research issues in DDs designs are discussed, and useful recommendations are provided. Effective means for drone source authentication, delivery package verification, operator authorization, and dynamic scenario-specific engagement are solicited for comprehensive DDS design for maximum security Received: 2023-03-07 Revised: 2023-04-27

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees.

Coffee arabica, originated in Ethiopia, is considered a quality bean for its high sensory qualities, and has a special price in the world coffee market. The country is a pool of genetic diversity for Arabica coffee, and coffee from different regions has a distinct flavor profile. Their exceptional quality is attributed to their genetic diversity, favorable environmental conditions, and agroforestry-based production system. However, the country still needs to benefit from its single-origin product due to a lack of appropriate traceability information to register for its geographical indication. Certification of certain plants or plant-derived products emerged to inform consumers about their exceptional qualities due to their geographical origin and protect the product from fraud. The recently emerging foodomics approaches, namely proteomics, genomics, and metabolomics, are reported as suitable means of regional agri-food product authentication and traceability. Particularly, the metabolomics approach provides truthful information on product traceability. Despite efforts by some researchers to trace the geographical origin of Ethiopian Arabica coffees through stable isotope and phenolic compound profiling and elemental analysis, foodomics approaches are not used to trace the geographical origin of Arabica specialty coffees from various parts of the country. A metabolomics-based traceability system that demonstrates the connection between the exceptional attributes of Ethiopian Arabica specialty coffees and their geographic origin is recommended to maximize the benefit of single-origin coffees.

DCAGS-IoT: Dynamic Cross-Domain Authentication Scheme Using Group Signature in IoT

Cross-domain authentication requires that there is no trust gap between different trust domains that can cause cross-domain devices to exceed the security control scope of the original trust domain and further expose cross-domain authentication systems to security threats. In addition, as relying on the traditional cross-domain authentication means built by centralized institutions cannot meet the data security needs in a big data environment. Therefore, it is necessary to design a secure dynamic cross-domain authentication scheme. In this paper, we propose a dynamic cross-domain authentication scheme (DCAGS-IoT) in the Internet of Things environment using the group signature technology and the distributed system architecture of blockchain. Specifically aiming at the problem of increasing and revoking users in dynamic cross-domain authentication, a user update algorithm with the complexity of O (logN) was designed to manage users in the trust domain. Moreover, we used the characteristics that group signature users can sign on behalf of a group to protect the users’ privacy and track suspicious users. Since the size of the signature generated by the scheme is independent of the number of group members N and only depends on the security parameters λ, the efficiency of the protocol implementation is improved, and the security and availability of the authentication scheme are guaranteed.

Towards a Catalogue of Self-Sovereign Identity Design Patterns

Self-sovereign identity (SSI) is a user-centric, decentralised identity approach that provides a means for identification, authentication, and authorisation without the involvement of external entities, responsible for identity provisioning and management in current centralised and federated approaches. In general, the basic building blocks of an SSI system include decentralised identifiers, verifiable credentials, identity wallets, a verifiable data registry, and three main actors: issuer, identity holder, and verifier. Even though the SSI field is dominated by proposals, SSI systems can be implemented in different ways, which is reflected in the absence of a well-defined architecture. Thus, the best implementation is still a matter of research, the requirements of the individual system, and its field of application. However, well-designed and implemented systems are crucial to avoiding failures, speeding up the development process, ensuring high quality, and the broader adoption of SSI solutions. Hence, the main objective of this study was to identify design patterns and good practices of the SSI ecosystems by reviewing and analysing the literature, technical documentation, and existing SSI implementations. Therefore, the study is built on existing knowledge, and presents a comprehensive catalogue of thirty-five SSI design patterns that can serve as a starting point for a possible SSI system design.

Smart Intrusion Detection System for CAN Network Implemented using LSTM Strategy

In today's cars, communication between electronic control units is managed via the Controller Area Network (CAN) bus system. Nevertheless, the CAN bus system lacks means for authentication and authorisation, making it susceptible to attacks like denial-of-service, fuzzing, and spoofing. To identify and counteract CAN bus network intrusions, this study suggests an intrusion detection system based on a Long Short-Term Memory (LSTM) model. Extraction of attack-free data and attack injection produced the dataset used for testing and training. Findings show that the suggested LSTM model has a greater detection rate than the Survival Analysis approach and is efficient in defending current automobiles from assaults on the CAN bus network. The ability to identify these sorts of assaults is limited by the constraints of conventional intrusion detection systems (IDS) for CAN bus networks, such as statistical analysis, frequency-based analysis, and Hidden Markov Model (HMM). Using a Long Short-Term Memory (LSTM) based strategy, the objective of this article is to build a reliable IDS that can efficiently identify and counteract these assaults on the CAN bus network.

Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices

Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.