Authentication Techniques Research Articles

A location‐aware physical unclonable function and Chebyshev map‐based mutual authentication mechanism for internet of surveillance drones

SummaryThe primary concern for securing the Internet of Drones (IoD) is authentication. Drone location is a vital information that must be verified before a secure communication link with the ground station can be established. In this paper, a location aware mutual authentication technique for secure inter‐drone and drone to ground communication using physical unclonable functions and Chebyshev chaotic maps is proposed. This is the first work that addresses the location validation of drones along with server‐less inter‐drone communication with a fail‐safe mechanism designed using a fuzzy inference system to tackle drone failures. The security of the system is analyzed using Burrows–Abadi–Needham logic, real‐or‐random oracle model and ProVerif. The processing, communication, and storage costs of the proposed scheme are determined and compared to other drone mutual authentication protocols. The proposed mechanism outperforms the existing mutual authentication protocols with superior security features and performance attributes and is well suited for surveillance drone networks.

Electrocardiogram (ECG)-Based User Authentication Using Deep Learning Algorithms.

Personal authentication security is an essential area of research in privacy and cybersecurity. For individual verification, fingerprint and facial recognition have proved particularly useful. However, such technologies have flaws such as fingerprint fabrication and external impediments. Different AI-based technologies have been proposed to overcome forging or impersonating authentication concerns. Electrocardiogram (ECG)-based user authentication has recently attracted considerable curiosity from researchers. The Electrocardiogram is among the most reliable advanced techniques for authentication since, unlike other biometrics, it confirms that the individual is real and alive. This study utilizes a user authentication system based on electrocardiography (ECG) signals using deep learning algorithms. The ECG data are collected from users to create a unique biometric profile for each individual. The proposed methodology utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to analyze the ECG data. The CNNs are trained to extract features from the ECG data, while the LSTM networks are used to model the temporal dependencies in the data. The evaluation of the performance of the proposed system is conducted through experiments. It demonstrates that it effectively identifies users based on their ECG data, achieving high accuracy rates. The suggested techniques obtained an overall accuracy of 98.34% for CNN and 99.69% for LSTM using the Physikalisch-Technische Bundesanstalt (PTB) database. Overall, the proposed system offers a secure and convenient method for user authentication using ECG data and deep learning algorithms. The approach has the potential to provide a secure and convenient method for user authentication in various applications.

Reversible demosaiced image authentication scheme with recoverability using clustering and matching techniques

In this paper, we propose a reversible and recoverable authentication technique for demosaiced images using the k-means clustering algorithm and side match technique. Prior methods generate the recovery codes directly using the most significant bits of sampled components or their compressed codes, which requires excessive bits to record them. This not only damages the image to a large extent, but also limits the quality of the recovered image. In this paper, the sampled components are clustered and their cluster indices are used as the recovery codes. A tampered block can mostly be recovered by other untampered ones with the same cluster index. For an unrecoverable block, the cluster indices of its neighboring untampered blocks are employed to predict the block, and the recovery is performed by using the side match technique. Since unrecoverable blocks are sparsely scattered in the tampered areas, the recovered blocks around them are mostly available, and therefore an accurate prediction can be made. The experimental results show that the average quality of marked and recovered images of our method increases by around 3.36 dB and 2.96 dB, respectively, compared to previous methods.

Blockchain-Based Authentication Scheme for Collaborative Traffic Light Systems Using Fog Computing

In the era of the Fourth Industrial Revolution, cybercriminals are targeting critical infrastructures such as traffic light systems and smart grids. A major concern is the security of such systems, which can be broken down into a number of categories, such as the authentication of data collection devices, secure data transmission, and use of the data by authorized and authenticated parties. The majority of research studies in the literature have largely focused on data integrity and user authentication. So far, no published work has addressed the security of a traffic light system from data collection to data access. Furthermore, it is evident that the conventional cloud computing architecture is incapable of analyzing and managing the massive amount of generated data. As a result, the fog computing paradigm combined with blockchain technology may be the best way to ensure data privacy in a decentralized manner while reducing overheads, latency, and maintaining security. This paper presents a blockchain-based authentication scheme named VDAS using the fog computing paradigm. The formal and informal verifications of the proposed solution are presented. The evaluation of the proposed scheme VDAS showed that it has low communication and computation costs compared to existing lightweight authentication techniques.

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

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

Lateral flow immunoassay-based absolute point-of-care technique for authentication of meat and commercial meat products.

The online version contains supplementary material available at 10.1007/s13197-022-05663-2.

A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID‐19 patients

AbstractCurrently, Internet of Medical Things (IoMT) gained popularity because of an ongoing pandemic. A few developed countries plan to deploy the IoMT for improving the security and safety of frontline workers to decrease the mortality rates of COVID‐19 patients. However, IoMT devices share the information through an open network which leads to increased vulnerability to various attacks. Hence, electronic health management systems remain many security challenges, like recording sensitive patient data, secure communication, transferring patient information to other doctors, providing the data for future medical diagnosis, collecting data from WBAN, etc. In addition, the sensor devices attached to the human body are resource‐limited and have minimal power capacity. Hence, to protect the medical privacy of patients, confidentiality and reliability of the system, the register sensor, doctor and server need to authenticate each other. Therefore, rather than two factors, in this work, a multifactor authentication protocol has been proposed to provide more secure communication. The presented scheme uses biometric and fuzzy extractors for more security purposes. Furthermore, the scheme is proved using informal and formal security verification BAN logic, ProVerif and AVISPA tools. The ProVerif simulation result of the suggested scheme shows that the proposed protocol achieves session key secrecy and mutual authentication

Lightweight and efficient privacy‐preserving mutual authentication scheme to secure Internet of Things‐based smart healthcare

AbstractIn recent years, Internet of Things (IoT) technology has been adopted in numerous application areas, such as healthcare, agriculture, industrial automation, and many more. The use of IoT and other technologies like cloud computing and machine learning has made the modern healthcare system to be smart, automated, and efficient. However, the continuous proliferation of cyber‐attacks on IoT devices has increased IoT challenges like data security, privacy protection, authentication, and so forth. In smart healthcare systems, due to the lack of authentication protocols, attackers can undermine the availability, confidentiality, and integrity of both smart healthcare devices and data, which can be life‐threatening in some situations. In this article, a privacy‐preserving mutual authentication scheme for IoT‐enabled healthcare systems is proposed to achieve lightweight and effective authentication of network devices. To support the processing capabilities of the IoT devices, this proposed authentication scheme is designed using lightweight cryptographic primitives, namely XOR, concatenation, and hash operation. The proposed scheme can establish a secure session between an authorized device and a gateway, and prevent unauthorized devices from getting access to healthcare systems. The security analysis and performance analysis assess the proposed authentication technique's effectiveness over existing well‐known schemes.

Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things (IoMT) enabled e-healthcare has the potential to greately improve conventional healthcare services significantly. However, security and privacy become major issues of IoMT because of the restricted processing abilities, storage, and energy constraints of the sensors. Therefore, it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors. In order to ensure security on sensitive medical data, effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers. In this view, this study designs an effective metaheuristic optimization based encryption with user authentication (EMOE-UA) technique for IoMT environment. This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity. Moreover, the EMOE-UA technique employs optimal multikey homomorphic encryption (OMKHE) technique to encrypt the IoMT data. Furthermore, the improved social spider optimization algorithm (ISSOA) was employed for the optimal multikey generation of the MKHE technique. The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects. The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

Route forecasting-based authentication scheme using A* algorithm in vehicular communication network

Researchers have developed several authentication techniques for route predictions based on user requirements. These techniques estimate the shortest path and available resources in vehicular communication networks. In the current research, the existing authentication techniques for vehicular communication are compared and their inadequacies are identified. Then, new authentication technique based on route forecasting are presented for vehicular communication networks, with the service provider anticipating alternate routes for customers if the current routes have more network traffic congestion. By presenting the most efficient route, the suggested model allows users to maximise their time efficiency. Using A* algorithm, VCN agent seeks path with less network traffic congestion. This algorithm determines the shortest path between a source and a destination. Users are provided with several options by the service provider. User accepts the finest option that meets their needs. This method allows the service provider to deliver at least 15 routes within three seconds. This strategy is beneficial when a significant number of vehicles are stuck in traffic and consumers require network resources to utilise their time effectively.

Application of Fourier transform infrared (FT-IR) spectroscopy and multivariate analysis for detection of adulteration in honey markets in Ethiopia

Honey is a highly susceptible food item to adulteration in national and international trade. Spectrum screening by FTIR coupled with multivariate analysis was investigated as an alternate analytical technique for honey adulterations and authentication. This technique was evaluated using pure honey samples that were blended at a ratio of 0–50% with commonly known adulterant materials and honey samples that were readily available for purchase in the Addis Ababa markets channel. Holeta Bee Research's bee farm pure honey, which is authentic honey, is employed as the control in this experiment. In the region, 4000–400 cm−1, spectral data of honey samples and five adulterant materials were recorded. The combination of spectra measurement with multivariate analyses resulted in the visualization of honey grouping and classification based on their functional group. The bands at 1800–650 cm−1 spectral region were selected for successful discrimination of clusters. Based on spectral differences, cluster analysis (CA) is also capable of grouping and separating pure from contaminated honey. Principle component analysis was able to visualize the differentiation of deliberately adulterated honey and commercially available from authentic ones. According to the results of our investigation, using FTIR analysis methods along with multivariate statistical analysis of the data could be considered useful fingerprinting procedures for identifying samples of pure and adulterated honey.

Benchmarking Neural Network Compression Techniques for Ocular-Based User Authentication on Smartphones

With the unprecedented mobile technology revolution, mobile devices have transcended from being the primary means of communication to an all-in-one platform. Consequently, an increasing number of individuals are accessing online services for e-commerce and banking via smartphones instead of traditional desktop computers. However, smartphones can be easily misplaced, lost, or stolen more often than other computing devices, thereby demanding effective user authentication mechanisms for device unlocking and secured transactions. Ocular biometrics has obtained significant attention from academia and industry because of its accuracy, security, and ease of use in mobile devices. Several studies have demonstrated the efficacy of deep learning models for ocular-based user authentication on smartphones. However, these high-performing models require enormous space and computational complexity due to the millions of parameters and computations involved. These requirements make their deployment on resource-constrained smartphones challenging. To this end, a handful of studies have been proposed for compact-size ocular-based deep-learning models to facilitate on-device deployment. In this paper, we conduct a thorough analysis of the existing neural network compression techniques applied as a standalone and in combination for ocular-based user authentication. Extensive experimental validation is performed on the two latest large-scale ocular biometric datasets collected using smartphones, namely, UFPR and VISOB 2.0 datasets. This study benchmarks the results of advanced compression techniques for further research and development in lightweight models for ocular-based user authentication on smartphones.

A Robust watermarking technique for biometric image authentication

A Robust watermarking technique for biometric image authentication

A robust watermarking technique for biometric image authentication

A robust watermarking technique for biometric image authentication

Physical-Layer Authentication Based on Channel Phase Responses for Multi-Carriers Transmission

To defend against eavesdropping and spoofing attacks, the physical layer authentication (PLA) techniques utilize the unique attributes of channel or device for identifying attackers. Among these techniques, the PLA schemes based on channel phase responses use the secret key driven channel phase to authenticate the legitimate user, which have a better performance than the channel amplitude based schemes. However, the prior phase-based schemes only consider the perfect channel correlation coefficient between the two successive timeslots, which differs from the real scenarios. Meanwhile, the closed-form expressions of the theoretical analysis results are not comprehensive in the prior schemes, which are also not tight especially at low signal-to-noise ratio (SNR) regions. In this paper, we propose a PLA scheme based on the channel phase response, aiming to offset the performance loss introduced by the channel correlation coefficient. Moreover, we derive the closed-form expressions of the theoretical analysis results, such as the mean value, the variance and the probability density functions (PDFs), which can be utilized to provide the closed-form threshold for making decision instead of a great ideal of testing. Then, the security analysis is provided to verify the resistance of the proposed scheme under the attacks. Simulation results show that the proposed scheme outperforms the benchmarks and the proposed theoretical results match well with the simulated results even under low SNR regions.

Survey Paper on Authentication and Voice Assistance Techniques to Assist Visually Challenged People

Survey Paper on Authentication and Voice Assistance Techniques to Assist Visually Challenged People

Secure authentication protocol for IoT applications based on blockchain technology

The lack of security in Internet of Things (IoT) infrastructure across different applications has attracted the attention of researchers to work on IoT security issues. The paper presents a scenario where blockchain technology is combined with IoT to provide a decentralized security mechanism. Secure authentication and Key-Agreement technique are proposed for IoT nodes and peers to ensure proper authorization before communication can take place. The proposed protocol uses public key cryptography to generate a shared symmetric key for mutual authentication and two-party conversation. The protocol was tested using Scyther and was found to be robust enough to withstand all known authentication-related attacks, including replay, and typing attacks. Hyperledger, a blockchain technology, was employed to provide a more efficient IoT-enabled infrastructure for the scalability of IoT devices on the network. The proposed technique provides a secure and scalable system for device authentication in a blockchain-enabled IoT environment.

A Privacy-Centered Protocol for Enhancing Security and Authentication of Academic Certificates

Academic certificate authentication is crucial in safeguarding the rights and opportunities of individuals who have earned academic credentials. This authentication helps prevent fraud and forgery, ensuring that only those who have genuinely earned certificates can use them for education and career opportunities. With the increased use of online education and digital credentials in the digital age, the importance of academic certificate authentication has significantly grown. However, traditional techniques for authentication, such as QR code, barcode, and watermarking, have limitations regarding security and privacy. Therefore, proposing a privacy-centred protocol to enhance the security and authentication of academic certificates is vital to improve the trust and credibility of digital academic certificates, ensuring that individuals' rights and opportunities are protected. In this context, we adopted the Challenge Handshake Authentication (CHA) protocol to propose the Certificate Verification Privacy Control Protocol (CVPC). We implemented it using Python and Flask with a Postgres database and an MVT structure for the application. The results of the implementation demonstrate that the proposed protocol effectively preserves privacy during the academic certificate issuance and verification process. Additionally, we developed a proof of concept to evaluate the proposed protocol, demonstrating its functionality and performance. The PoC provided insights into the strengths and weaknesses of the proposed protocol and highlighted its potential to prevent forgery and unauthorised access to academic certificates. Overall, the proposed protocol has the potential to significantly enhance the security and authenticity of academic certificates, improving the overall trust and credibility of the academic credentialing system.

An Improved Two-Factor Authentication Scheme for Healthcare System

Patients can communicate with their doctors from the comfort of their own homes. The Telecare Medicine Information Service is one of the most efficient, secure, and protective forms of communication. It maintains and provides patients' Electronic Medical Records (EMR), allowing doctors to track their patients and ensure they receive correct healthcare services. Therefore, the key concerns to stop unauthorised users from obtaining health information are security and privacy. There are several authentication methods in use today, but they are all susceptible to different kinds of attacks. We enhanced a two-factor-based authentication technique to increase security and privacy while addressing earlier shortcomings. We simulate our proposed scheme using the Automated Validation of Internet Security Protocols and Applications tool (AVISPA) tool that confirms that scheme is secure against active and passive attacks including replay and man-in-the-middle attacks. Our proposed protocol provides high security and better complexity based on communication, computation costs and execution time.

Behavioral biometric authentication on smartphone using keystroke dynamics

Over the past few years, smartphones have revolutionized our life. Authentication plays important role as all private and business data are stored in smartphone. Password authentication is not secure technique as password can be easily stolen by hackers. Hence, need to use advance technology to increase the security of authentication. Keystroke Dynamics is behavioral based authentication technique. It identifies the user identity through their typing behavior to check user is genuine or imposter. In this paper author is presenting a KDSP system for Smart devices to increase the security of authentication at login time. On the basis of timing feature set, typing speed, flight time and error rate of keystroke dynamics author is checking the identity of the user. Proposed system works on all the Android smartphones and tablets. All experimental work was performed on 268 subjects to capture keystroke data on server. In Statistical analysis, Equal-error-rate (EER) is measured 7.415% of the 60 subjects for False acceptance rate (FAR) and 208 subjects for False rejection rate (FRR).