Authentication Techniques Research Articles

A Survey of Parallel Message Authentication and Hashing Methods

Background:
 Currently, there are approximately 4.95 billion people who use the Internet. This massive audience desires internet shopping, information exchange, social networking, and other activities that have grown dramatically in recent years. Therefore, it creates the need for greater confidentiality and privacy. In recent days, fraud via the Internet has been one of the key impediments to the dissemination of the use of business apps. Therefore, the three important security concerns actually occur daily in our world of transparent fashion, more accurately: identity, authentication, and authorization. Identification is a procedure that permits the recognition of an entity, which may be a person, a computer, or another asset such as a software programmer.
 Materials and Methods:
 In security systems, authentication and authorization are two complementary procedures for deciding who may access the information resources across a network. Many solutions have been presented in the literature. To get more performance on the authentication algorithmic, researchers used parallelism to increase the throughput of their algorithms. On the one hand, various approaches have been employed to enhance the security of cryptographic systems, including increasing the number of rounds, utilizing substitution tables, and integrating other security primitives for encryption and message authentication.
 Results:
 Recent studies on parallel message authentication and hashing algorithms have demonstrated that GPUs outperform other parallel platforms in terms of performance.
 Conclusion:
 This work presents a parallel implementation of message authentication techniques on several platforms. It is studying and demonstrating works which discuss authentication, hashing, and their implementation on a parallel platform as a main objective.

Reversible Linguistic Steganography With Bayesian Masked Language Modeling

Text authentication serves a vital role in the defense of digital identity and content against various types of cybercrime. The use of a digital signature is a common cryptographic technique for text authentication. Linguistic steganography can be applied to further conceal a digital signature within the corresponding text to facilitate data management. However, steganographic distortion lurking in the text, albeit almost imperceptible, has the potential to cause automatic computing machinery to make biased decisions. This has led to an interest in the pursuit of reversibility, the ability to reverse a steganographic process and remove distortion. In this article, we propose a reversible steganographic system for natural language text. We use a pre-trained transformer neural network for masked language modeling and embed messages in a reversible manner via predictive word substitution. Furthermore, we derive an adaptive steganographic route by taking account of predictive uncertainty, which is quantified based on a theoretical framework of Bayesian deep learning. Experimental results show that the proposed steganographic system can attain a proper balance between capacity, imperceptibility, and reversibility with close semantic and sentimental similarities between cover and stego texts.

Lightweight Flexible Group Authentication Utilizing Historical Collaboration Process Information

Existing device authentication techniques may suffer from heavy communication, computation, and storage overhead for identifying a growing number of devices in collaborations. This paper proposes a novel group authentication (GA) method for decentralized edge collaboration by exploiting the historical collaboration process information, i.e., the distributed learning parameters and results from the previous round of collaboration. Two strategies are developed to generate tokens locally at the edge devices’ side for mutual authentication, named random token generation (R-TG) and privacy-preserving token generation (PP-TG). Specifically, the R-TG strategy randomly selects several historical learning parameters as tokens, while the PP-TG strategy designs a one-way function to defend against privacy leakage by concealing the historical information. A GA protocol is proposed, where each device simultaneously authenticates the others in the same group by repeating the learning process using their tokens. If the process converges to an expected result, all the devices are authenticated as legitimate group members at once. The proposed scheme provides a lightweight flexible solution without pre-generating and distributing any keys/secrets operating on top of a standardized security protocol, and protects the collaboration continuously. The simulation results demonstrate the viability of our scheme and its superior performance compared to several benchmark schemes.

Efficient Authentication in RFID Devices Using Et Als Algorithm

Abstract: Security plays a vital role during the transmission of private data from one sender to the other. Although there are many security algorithms implemented but here we are providing the security algorithms on the RFID devices. The authentication techniques implemented in RFID is based on the new algorithm based on smart cards. The data send through the tags can be made secure using the proposed algorithm so that the unauthorized users can’t access the data without any further unique numbers

Reputation based Symmetric Key Authentication for Secure Data Transmission in Mobile Ad Hoc Networks

INTRODUCTION: MANETs were a group of nodes which connected each other to establish the network via wireless association to forward packets towards destinations. OBJECTIVES: Reputation-based Symmetric Key Authentication (RSKA) technique is proposed to improve the security of data communication, which securely transmits data packets within MANET communication. Initially, a reputation counts-based node authentication algorithm is designed in RSKA technique by considering the reputation value of mobile nodes in networks. METHODS: By using a reputation count-based node authentication algorithm, the RSKA technique performs the node authentication process before initiating route identification and selects cooperative nodes in MANETs for data transmission. Therefore, the RSKA of nodes results in improved security of data communication for enhancing throughput and reducing data loss rate. Besides, the RSKA technique uses symmetric key cryptography for secure data transmission in which a secret key is generated for the data to be transmitted by the corresponding mobile node along the route path of the source-destination pair. The destination node having the secret key can only decrypt the cypher text. RESULTS: Thus, the RSKA technique improves the data confidentiality rate as well as minimizes the time to protect the data transmission in an efficient way. RSKA was calculated by various parameters, namely data loss rate, throughput and time to protect data delivery as well as data confidentiality rate. CONCLUSION: The simulation result of RSKA improves the data confidentiality rate for achieving secured data transmission as well as minimizes time to protect data delivery within MANETs compared with conventional methods.

Secure multi-factor access control mechanism for pairing blockchains

Digitalization has made it easier for centric systems to transition from single-factor to two-factor authentication. However, several access control practices struggle to authenticate users correctly due to their weak and outdated authorization systems. Moreover, the majority of cloud-centric models suffer from security and single-point-of-failure issues. Consequently, several systems have migrated their access control services to the blockchain. A good illustration is using two-factor authentication techniques to increase transaction security in Bitcoin systems. However, with this holistic trend of 2FAs, multi-factor authentication has yet to see this trend. In this regard, the study proposes an access control technique that combines multiple factors of knowledge, inherent, and possession to authenticate users to the blockchain. The multiple factors derive a time-based access code for users to generate private keys. The efficiency of the proposed method is tested with a Py-Eth-pairing library to determine the computation cost and transmission overhead. Furthermore, we use the EIP (Ethereum Improvement Proposals) library to assess the gas cost and determine the throughput. Our findings show the suggested approach to achieving the lowest operational cost, making it scalable on the blockchain while demonstrating its practicability through the model framework.

Efficient Authentication Scheme for 5G-Enabled Vehicular Networks Using Fog Computing.

Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems incur exceptionally high communication and processing costs. This study proposes a CPPA method based on fog computing (FC), as a solution for these issues in 5G-enabled vehicle networks. In our proposed FC-CPPA method, a fog server is used to establish a set of public anonymity identities and their corresponding signature keys, which are then preloaded into each authentic vehicle. We guarantee the security of the proposed FC-CPPA method in the context of a random oracle. Our solutions are not only compliant with confidentiality and security standards, but also resistant to a variety of threats. The communication costs of the proposal are only 84 bytes, while the computation costs are 0.0031, 2.0185 to sign and verify messages. Comparing our strategy to similar ones reveals that it saves time and money on communication and computing during the performance evaluation phase.

Application of Authentic Assessment Techniques in Thematic Learning

The purpose of this study is to explain the skills assessment techniques in thematic learning at Madrasah Ibtida'iyah Al-Hidayah Mangli Jember for the 2021/2022 academic year. The approach used in this research is a qualitative approach and the type of research used is phenomenology. The data source used in this study to determine the data subject with Purposive technique. Data collection techniques using observation, interviews and documentation. Data analysis uses the theory of Miles, Huberman and Sardana. The results of this study The application of authentic assessment techniques in thematic learning at MI Al-Hidayah Mangli Jember 2021/2022 is 1. the application of MI Al-Hidayah Mangli attitude assessment techniques includes; observation, self-assessment and assessment between friends. At the observation assessment stage, the educator has an observation note. Journal assessment educators do not use it. Educators' self-assessment uses a questionnaire. The stages of assessment between friends educators know it only from information from students. 2. the application of knowledge assessment techniques in MI Al-Hidayah Mangli includes; written tests, oral tests and assignments. At the written test stage, educators do it with questions. Stages of oral test assessment educators often do at the end of learning. Stages of assignment assessment educators give homework. 3. the application of MI Al-Hidayah Mangli skill assessment techniques includes; performance, and portfolio. The performance stage of the educator does it by making a skill. Stages in project assessment educators do not do it. Stages in portfolio assessment educators often collect works from students.

Palmprint Authentication Technique Based on Convolutional Neural Network

Palmprint Authentication Technique Based on Convolutional Neural Network

ANAA-Fog: A Novel Anonymous Authentication Scheme for 5G-Enabled Vehicular Fog Computing

Vehicular fog computing enabled by the Fifth Generation (5G) has been on the rise recently, providing real-time services among automobiles in the field of smart transportation by improving road traffic safety and enhancing driver comfort. Due to the public nature of wireless communication channels, in which communications are conveyed in plain text, protecting the privacy and security of 5G-enabled vehicular fog computing is of the utmost importance. Several existing works have proposed an anonymous authentication technique to address this issue. However, these techniques have massive performance efficiency issues with authenticating and validating the exchanged messages. To face this problem, we propose a novel anonymous authentication scheme named ANAA-Fog for 5G-enabled vehicular fog computing. Each participating vehicle’s temporary secret key for verifying digital signatures is generated by a fog server under the proposed ANAA-Fog scheme. The signing step of the ANAA-Fog scheme is analyzed and proven secure with the use of the ProfVerif simulator. This research also satisfies privacy and security criteria, such as conditional privacy preservation, unlinkability, traceability, revocability, and resistance to security threats, as well as others (e.g., modify attacks, forgery attacks, replay attacks, and man-in-the-middle attacks). Finally, the result of the proposed ANAA-Fog scheme in terms of communication cost and single signature verification is 108 bytes and 2.0185 ms, respectively. Hence, the assessment metrics section demonstrates that our work incurs a little more cost in terms of communication and computing performance when compared to similar studies.

Securing Access to Internet of Medical Things Using a Graphical-Password-Based User Authentication Scheme

Digital healthcare services have seen significant growth in this decade and many new technologies have been thoroughly examined to provide efficient services through secure infrastructures. The Internet of Medical Things (IoMT) revitalizes a healthcare infrastructure by creating an interconnected, intelligent, accessible, and efficient network. While there have been many studies on possible device authentication techniques for the IoMT, there is still much work to be done in user authentication to provide sustainable IoT solutions. Graphical passwords, which use visual content such as images instead of traditional text-based passwords, can help users authenticate themselves. However, current schemes have limitations. Therefore, this paper proposes a novel graphical authentication scheme that uses multiple factors to register and authenticate users using simple arithmetic operations, machine learning for hand gesture recognition, and medical images for recall purposes. The proposed method is designed to keep the authentication process simple, memorable, and robust. To evaluate the proposed scheme, we use the Post-Study System Usability Questionnaire (PSSUQ) to compare it with PIN-based and pattern-based authentication techniques. While comparing treatment and comparison groups, system quality showed a 16.7% better score, information quality a 25% increase, interface quality a 40% increase, and overall quality showed a 25% increase. The proposed method successfully revitalizes the use of graphical passwords, specifically in the field of IoMT, by developing a user-friendly, satisfying, and robust authentication scheme.

Stationary mobile behavioral biometrics: A survey

Current security mechanisms in mobile devices such as PINs, passwords, patterned passwords, and biometrics are one-time entry-point authentication and vulnerable to attacks. Furthermore, advanced mechanisms like Multi-Factor Authentication (MFA) introduce friction in the user experience. In contrast, behavioral biometrics rely on user interaction with computing devices to authenticate a user and thus, can be continuous, non-intrusive, and cost-effective, representing a promising direction that complements existing authentication techniques. This survey focuses on stationary/non-walking (sitting, standing) mobile behavioral biometrics through motion events like acceleration, gyroscope, magnetometer, and orientation (rotation) with the optional support of other non-motion, sporadic modalities such as swipes and keystrokes. The focus on stationary behaviors can be justified because such behaviors represent the major way a user interacts with mobile devices. To help readers understand the broad landscape of user activities/behaviors, we categorize the state of the art into natural and designed behaviors and describe the underpinning of behavioral biometrics in cognitive psychology. Furthermore, we categorize the surveyed studies into three groups based on the fusion of motion modalities and characterize each study along dimensions such as task, datasets, modality, algorithms, and performance. Based on our survey, we identify several future directions of research.

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.

Techniques for Food Authentication: Trends and Emerging Approaches

Food producers and retailers are obliged to provide correct food information to consumers; however, despite national and international legislation, food labels frequently contain false or misleading statements regarding food composition, quality, geographic origin, and/or processing [...].

Lightweight authentication scheme for massive MIMO on Internet of Things connectivity

Massive MIMO is an essential technology in developing 5G networks and a concept that may be applied to other wireless systems. However, the advantages of adopting massive MIMO for broadband communication are well-established. Recently researcher has been devoted to building communication systems sustaining high communication rates with security. While massive MIMO for Internet-of-Things (IoT) connectivity is still a developing issue, IoT connectivity has requirements and limitations that differ significantly from broadband connections. Although IoT makes people’s lives easier by allowing physical devices to flow through, the interaction of open wireless channels such as Bluetooth, ZigBee, LoRa, Narrowband-Internet of Things (NBIoT), and WiFi, has produced various security and privacy difficulties. Identity authentication is one of the effective solutions for addressing the Internet of Things security and privacy concerns. The typical point-to-point authentication technique ignores the internet of things’ massive number of nodes and limited node resources. Group authentication is an authentication method that simultaneously confirms the identity of a group of members, offering a novel approach to identity identification for the internet of things nodes. However, existing group authentication systems appropriate for the internet of things scenarios pose security issues. They cannot withstand malicious attacks such as forging and replay and cannot prevent group managers from fooling group members. Most existing group authentication schemes are computationally expensive and cannot be applied to resource-constrained IoT scenarios. At the same time, existing systems based on secret-sharing technology cannot resist forgery attacks and replay attacks. The attacker can forge a legal token by modifying the Lagrangian coefficient in the authentication token to pass the group authentication. This work employs verifiable secret sharing technology to create a lightweight verifiable group authentication method (L-IoT-GS) suitable for Internet of Things situations to resist group managers’ deceptive group behavior. Nodes in the Internet of Things scenario can frequently join and leave the network. Because of this, this article proposes a critical update link based on a verified group authentication system for updating group Member rights. According to security analysis, the suggested L-IoT-GS scheme meets accuracy and confidentiality requirements and can withstand malicious attacks such as replay, forgery, and impersonation. Furthermore, performance study and experimental simulation reveal that the L-IoT-GS technique minimizes group members’ computing costs compared to existing standard IoT group authentication schemes.

Naïve Bayes Supervised Learning based Physical Layer Authentication: Anti-Spoofing techniques for Industrial Radio Systems

Physical Layer Security (PLS) based authentication schemes are an alternative to conventional security schemes such as e.g. certificates or Message Authentication Codes (MACs). They can provide a more lightweight solution compared to traditional cryptography in order to meet the requirement of secure data transmission. However, errors in Physical Layer Authentication (PLA) techniques can occur due to adverse influences resulting from PLS schemes such as receiver noise or fading channels. Skillfull methods are therefore required in order to detect anormal system behaviour. One promising solution are supervised classification schemes. The application of Naïve Bayes (NB) classifiers for PLA is therefore proposed and evaluated within this work. Prior to that, we analyse the resource efficiency within typical Ultra Reliable Low Latency Communication (URLLC) applications and conduct a security overhead analysis. We propose strategies in order to overcome the problem of missing training data from either the URLLC user or attacker node. A real world Software Defined Radio (SDR) based testbed using OFDM (Orthogonal Frequency Division Multiplexing) is implemented in order to evaluate the performance of NB based PLA. The measurements are conducted within the german campus network frequency band (LTE band 43). Further, we conduct a hyperparameter optimization (HPO) based on random search. The investigated classifiers show promising results in terms of authentication accuracy and Receiver Operating Characteristic (ROC) curve performance.

Multi-target trajectory planning and control technique for autonomous navigation of multiple robots

The autonomous robot has been the attraction point among robotic researchers since the last decade by virtue of increasing demand of automation in defence and intelligent industries. In the current research, a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace. Here, a hybrid algorithm is adopted for designing the controller with consideration of navigational parameters. A Petri-Net controller is also aided with the developed controller to resolve any conflict during navigation. The developed controller has been investigated on WEBOTS and MATLAB simulation environments coupled with real-time experiments by considering Khepera-II robot as wheeled robot. Single robot- multi-target, multiple robot single target and multiple robots-multiple target problems are tackled during the investigation. The outcomes of simulation are verified through real-time experimental outcomes by comparing results. Further, the proposed algorithm is tested for its suitability, precision, and stability. Finally, the developed controller is tested against existing techniques for authentication of proposed technique, and significant improvements of an average 34.2% is observed in trajectory optimization and 70.6% in time consumption.

CKDAN: Content and keystroke dual attention networks with pre-trained models for continuous authentication

With the rapid development of digitalization, information security becomes more and more important. User authentication is a significant line of defense. During the past few years, many research works concentrating on user authentication have been published. Traditional authentication techniques (password, ID card-based authentication, or other biometrics like fingerprint or iris) basically fall into the category of one-time authentication. The disadvantage of these methods is that once an unknown user completes an authentication through forgery, the system would continue to operate without any resistance, thus putting the entire system at risk. Recently, due to the prevalence of deep learning techniques, some researchers have applied learning-based models to user authentication. However, almost all existing methods only focus on keystroke dynamics while ignoring the ”text” entered by users during keystrokes. In this paper, we propose contents and keystroke dual attention networks with pre-trained models for continuous authentication. To the best of our knowledge, our paper is the first to address user-inputted ”text” during keystrokes as an important asset beyond traditional characteristic keystroke dynamics. Specifically, we use the well-known pre-trained RoBERTa model to capture textual features. Then, we pass textual features and conventional features through our proposed dual attention networks. Our networks fuse these features and acquire final representations. Experiments show that the CKDAN model achieves state-of-the-art performance on two datasets, Clarkson II keystroke dataset and Buffalo dataset, outperforming all baseline methods.

BlockEdge: A Privacy-Aware Secured Edge Computing Framework Using Blockchain for Industry 4.0

Edge computing has its application in a lot of areas now, but with the increasing popularity and benefits, it suffers from some challenges such as data privacy and security. Intruder attacks should be prevented and only authentic users should have access to data storage. Most of the authentication techniques apply some trusted entity to undergo the process. Users and servers both have to be registered in the trusted entity to get permission of authenticating other users. In this scenario, the entire system depends on a single trusted entity; so, a single point of failure can cause the failure of the total system, and scalability issues are there also. To address these issues remaining in the existing systems, in this paper, a decentralized approach has been discussed which is capable of eliminating the concept of a single trusted entity by introducing a blockchain paradigm in edge computing where every time a user or server wants to enter the system, it does not have to register itself manually, but the authentication process is carried out throughout the scheme automatically. Experimental results and performance analysis prove that the proposed architecture is definitely beneficial and it outperforms the existing ones in the concerned domain.

Impact of Blockchain Technology in Modern Banking Sector to Exterminate the Financial Scams

The paper examined the use of blockchain in banking sector. With the increase of user’s interest in banking sector, transactions are conducted online or via physical credit scanners. Banking sector is easy target for the hackers. Unauthorized person can hostage the bank data by using cyber security threats such as phishing attack, Ransomware attack, and Denial of Service (DoS). The only solution to secure the customer precious data is blockchain. It has the potential to considerably decrease costs and it can completely alter the banking industry. The present centralized banking system can be strengthened using blockchain technology. Due to the transparency, auditability, immutability, operational resilience and data encryption essential in blockchains, it can secure the cyber security, forbid crooked actions and perceived tampering of data. Blockchain technology will deal the movement in the banking industry and associated facilities in prevailing area. Correspondingly inspecting cases both at home and abroad, it might be recognized areas that blockchains are used aggressively, utilized in banking sections are growing into remittance, resolution, smart contracts and reliability. Multi-factor authentication (MFA) technique is used to find the legitimate users by demanding a user to conform multiple identification information. The username and password can easily be hacked by unauthorized person, so we create a new authentication method that can’t be hacked with the help of Blockchain Techn[1]ology.
 
 1Deputy Director Quality Assurance Agency, Higher Education Commission, Islamabad, Pakistan.
 2Deptt. Of CS & IT, The IUB, Bahawalpur, Pakistan
 3 Department of Management Sciences, Shifa Tameer-e-Millat University, Islamabad.