Authentication Accuracy Research Articles

VocalPrint: A mmWave-Based Unmediated Vocal Sensing System for Secure Authentication

With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VocalPrint</i> , a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VocalPrint</i> exploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the vocal tract and vocal source features and input them into an ensemble classifier for authentication. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VocalPrint</i> is practical as it allows the effortless transition to a smartphone while having sufficient usability due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VocalPrint</i> achieves over 96 percent authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like, fingerprint or face. Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’ identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session. However, divergent issues remain unaddressed. This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called, Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s (GWCK-CC). First, a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise present in the raw input face images. Cauchy Kriging Regression function is employed to reduce the dimensionality. Finally, Continuous Czekanowski’s Classification is utilized for proficient classification between the genuine user and attacker. By this way, the proposed GWCK-CC method achieves accurate authentication with minimum error rate and time. Experimental assessment of the proposed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset. The results confirm that the proposed GWCK-CC method enhances authentication accuracy, by 9%, reduces the authentication time, and error rate by 44%, and 43% as compared to the existing methods.

Physical Layer Authentication Based on Channel Polarization Response in Dual-Polarized Antenna Communication Systems

This study presents a novel approach for physical layer authentication based on channel polarization response (CPR). CPR is sensitive to variation in the physical properties of scatterers, and the CPR difference between various channels is higher than the channel frequency response (CFR) under rich scattering scenarios. Additionally, the estimation of CPR is continuous, the authentication interval can be adjusted according to the channel coherence time, then the proposed scheme can be applied to any rich scattering scenarios, including highly dynamic scenarios. Since the received polarization state is fixed during the channel coherence time, we can coherently stack the received polarization state to improve the signal to noise ratio (SNR) and the estimation accuracy of CPR, thereby achieving high authentication accuracy under ultra-low SNR. Moreover, since the transmitted polarization state of various transmitters is different, because of their unique hardware deficiencies, and since the CPR is dependent on the transmitted polarization state, the CPR of other transmitters is different, allowing the resolution of co-located attacks. We theoretically drive the false alarm probability, detection probability, optimal discriminant threshold, computational complexity, optimal stacking numbers, and optimal CPR points for authentication. Furthermore, extensive simulations and experiments are performed to verify the validity and effectiveness of the proposed scheme.

Multiuser Physical-Layer Authentication Based on Latent Perturbed Neural Networks for Industrial Internet of Things

Recently, learning (DL)-based physical-layer authentication (PLA) has attracted much attention since artificial neural networks (ANNs) can be built to extract useful features from complex wireless environments, thus achieving high authentication performance and lightweight deployment in mobile edge computing (MEC)-Industrial Internet of Things (IIoT) scenario. However, the low latency characteristic of MEC makes it impossible to have much time to obtain sufficient signals for training the authentication system, which will cause over-fitting issues and deteriorate the authentication performance. Data augmentation is an effective method to address this problem. However, existing PLA with data augmentation can not generate representative and high-quality samples, consequently lacking generality in the actual identity authentication. To tackle this problem, a novel channel impulse response (CIR)-based multiuser authentication named latent perturbed neural networks (LPNNs) is proposed in this article, aiming at achieving high authentication performance even when trained a few data. Instead of relying on the generation of synthetic samples, the proposed LPNN adds Gaussian noise in the smooth latent space to avoid underdetermined and poor generalization, which has better interpretability. Specifically, to obtain a better understanding than a black box that connects input CIRs to authentication results, we defined Fingerprint Library and provided post-hoc explanations to answer the following question: which library examples explain the authentication results issued for a given CIR sample? Moreover, the simulations under the static and dynamic IIoT scenarios verify the superiority in authentication accuracy of the proposed LPNN over vanilla deep neural network (DNN) and convolutional neural network (CNN).

Remora Optimization Based Sample Weighted Random SVM For Human Gait Authentication

In this paper, we present a novel ESVM-SWRF method for authenticating human using a gait cycle. The different covariates related to walking are analyzed and investigated. The walking speed of people may change due to the individual body structure, gender, and age thereby creating a complex situation. Based on the studies over past decades, different perspectives with cross-speed gait authentication were suggested. The factors influencing the identification of gait are some of the covariate factors namely walking speed, injuries, walking surface, viewpoint, and clothing. Our proposed work uses an effective dataset CASIA-C. Most of the existing techniques achieved a nearly 100% authentication accuracy rate for normal walking conditions but their performance is not optimal when applied under different covariate conditions. Our proposed work proves a high accuracy rate of 89% for different covariate conditions compared to other existing methods.

HeartPrint: Exploring a Heartbeat-Based Multiuser Authentication With Single mmWave Radar

Continuous authentication is crucial for protecting user’s privacy throughout their login session. Existing studies employ wireless sensing technologies to provide device-free and unobtrusive authentication; the user’s behavior is continually assessed without their direct involvement until it deviates from their normal pattern. However, these works primarily concentrate on single-user authentication, which poses challenges in multiuser scenarios, such as smart homes and offices, where more than one user usually exists. In this article, we propose HeartPrint, a continuous multiuser authentication system, that employs a single commodity mmWave radar to capture the unique self-driving heartbeat motions from multiple users. Specifically, HeartPrint leverages the effect of skin surface vibrations caused by heartbeat on radio frequency (RF) transmissions. To profile individual heartbeat signals from the entangled components that are induced by multiple users, we first use a clustering method to position each user in the environment, then focus on the signal reflected from each position separately. The irrelevant body movements are eliminated from the RF signal by using a proposed signal energy comparison method for preserving fine-grained heartbeat traits. We then develop a pipeline to extract the most informative features for characterizing each user and feed them to an elaborated classifier for user authentication. We evaluate HeartPrint with 54 participants and demonstrate that it achieves an average authentication accuracy of over 95%. Additionally, we show that it is resilient against spoofing attacks, with an average attack success rate of less than 3%.

Partial palm vein based biometric authentication

Palm vein biometrics is a potential authentication technique. In addition to the inherent liveness property, the palm vein pattern exhibits high authentication accuracy and anti-spoofing capability due to the dense vein pattern and broad coverage area. However, the large palm size increases the acquisition device’s cost and size and significantly hinders device miniaturization, resulting in the limited applicability of palm vein biometrics. A solution to this problem is to restrict the area of the palm vein pattern required for authentication. Towards this, in this paper, we explore the potential of partial palm vein patterns for authentication. Through experimentation with varied-sized partial palm vein images, we show that (14)th of palm vein pattern is adequate for individual authentication. Based on this observation, we propose an authentication framework using partial palm vein patterns. The system takes (14)th palm vein pattern as input in both the enrollment and authentication phase, and partial to full image matching is performed. We propose a rule-based image acquisition and stitching method for accurately converting partial images into full images during enrollment. The proposed partial palm vein authentication framework using elastic matching achieved an Equal Error Rate (EER) of 0.0202%, 0.1919%, and 0.2282% on the CASIA, VERA, and PUT datasets, respectively. Experiments using deep feature matching also gave comparable results to elastic matching.

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

The usage of mobile phones has increased multi-fold in recent decades, mostly because of their utility in most aspects of daily life, such as communications, entertainment, and financial transactions. In use cases where users’ information is at risk from imposter attacks, biometrics-based authentication systems such as fingerprint or facial recognition are considered the most trustworthy in comparison to PIN, password, or pattern-based authentication systems in smartphones. Biometrics need to be presented at the time of power-on, they cannot be guessed or attacked through brute force and eliminate the possibility of shoulder surfing. However, fingerprints or facial recognition-based systems in smartphones may not be applicable in a pandemic situation like Covid-19, where hand gloves or face masks are mandatory to protect against unwanted exposure of the body parts. This paper investigates the situations in which fingerprints cannot be utilized due to hand gloves and hence presents an alternative biometric system using the multimodal Touchscreen swipe and Keystroke dynamics pattern. We propose a HandGlove mode of authentication where the system will automatically be triggered to authenticate a user based on Touchscreen swipe and Keystroke dynamics patterns. Our experimental results suggest that the proposed multimodal biometric system can operate with high accuracy. We experiment with different classifiers like Isolation Forest Classifier, SVM, k-NN Classifier, and fuzzy logic classifier with SVM to obtain the best authentication accuracy of 99.55% with 197 users on the Samsung Galaxy S20. We further study the problem of untrained external factors which can impact the user experience of authentication system and propose a model based on fuzzy logic to extend the functionality of the system to improve under novel external effects. In this experiment, we considered the untrained external factor of ‘sanitized hands’ with which the user tries to authenticate and achieved 93.5% accuracy in this scenario. The proposed multimodal system could be one of the most sought approaches for biometrics-based authentication in smartphones in a COVID-19 pandemic situation.

AcoPalm: Acoustical Palmprint-Based Noncontact Identity Authentication

Biometric sensing has become a widely concerned authentication technology. Existing image-based methods are susceptible to light conditions and have privacy exposure risks, while contact authentication methods are not conducive to epidemic prevention requirements in public places. In this article, we propose a palmprint-based identification system by collecting backscattered signals of the inaudible acoustic signals, namely AcoPalm. AcoPalm does not require special hardware and contact operation for user authentication. First, frequency modulated continuous wave (FMCW) on acoustic signals are designed to extract static contours and palmprint changes and to model the unique biological characteristics of the individual palm. Second, a palmprint authentication model based on PENN is proposed to achieve high-precision multiuser authentication without mass training data. Finally, the system performance is evaluated in multiple smartphones and three scenarios. The experimental results show that AcoPalm can resist replay attack and imitation attack, and the authentication accuracy can reach 96.22%. Furthermore, AcoPalm achieves satisfactory experience in availability and practicality.

A smartphone authentication system based on touch gesture dynamics

A smartphone authentication system based on touch gesture dynamics

Isogency Hosmer–Lemeshow Logistic Regression-Based Secured Information Sharing for Pharma Supply Chain

Counterfeit drugs are forgery-tagged medicines that are considered to be drugs without vigorous active pharmaceutical ingredients (API). India, being the world’s largest producer of drugs, faces a crucial issue of counterfeits. Moreover, counterfeits identify their path into the pharmaceutical supply chain (PSC) effortlessly owing to the dearth of security and traceability in the prevailing system. This is because the software applications currently in use stockpile the information about drugs on centralized servers and are accessed by manufacturers, distributors and retailers via the internet. The security of such systems is found to be weak. To address these issues, in this work, a novel method called Supersingular Isogeny and Hosmer–Lemeshow Logistic Regression-based (SI-HLLR) secured information sharing for the pharmaceutical supply chain is proposed. The SI-HLLR method is split into two sections, block validation and authentication. First, with the pharmaceutical sales data provided as input, the supersingular isogeny Diffie–Hellman key exchange model is applied for block validation and then is implemented using a blockchain. Next, with the validated blocks, the authentication mechanism is performed using Hosmer–Lemeshow logistic regression-based authentication that in turn eliminates the counterfeit drugs from the pharmaceutical supply chain. The hyperledger fabric blockchain solution using SI-HLLR leads to improved security ensuring data integrity and better authentication accuracy in the proposed method.

User Authentication Method Based on Keystroke Dynamics and Mouse Dynamics with Scene-Irrelated Features in Hybrid Scenes.

In order to improve user authentication accuracy based on keystroke dynamics and mouse dynamics in hybrid scenes and to consider the user operation changes in different scenes that aggravate user status changes and make it difficult to simulate user behaviors, we present a user authentication method entitled SIURUA. SIURUA uses scene-irrelated features and user-related features for user identification. First, features are extracted based on keystroke data and mouse movement data. Next, scene-irrelated features that have a low correlation with scenes are obtained. Finally, scene-irrelated features are fused with user-related features to ensure the integrity of the features. Experimental results show that the proposed method has the advantage of improving user authentication accuracy in hybrid scenes, with an accuracy of 84% obtained in the experiment.

Analysis of Malicious Node Identification Algorithm of Internet of Vehicles under Blockchain Technology: A Case Study of Intelligent Technology in Automotive Engineering

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle’s identity and message is determined to protect the vehicle’s security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment.

Block chain enabled auditing with optimal multi‐key homomorphic encryption technique for public cloud computing environment

SummaryCloud storage is a significant cloud computing (CC) service which enables the client to save and retrieve the data at anytime and anywhere. Due to the increased demand and familiarity of the CC environment, different kinds of security threats and susceptibilities are raised. Data integrity and privacy are the major problems in CC environment where the data can be saved in distinct geographic regions. So, privacy preservation and data integrity become important factors of the user concern related to the CC environment. Several auditing protocols are majorly dependent upon the conventional public key infrastructure, which led to high computational complexity and it is unsuitable for the setting of multiple users. To resolve these issues, this study develops a new block chain enabled auditing with optimal multi‐key homomorphic encryption (BEA‐OMKHE) technique for public cloud environment. The proposed BEA‐OMKHE technique aims to assure data integrity, security, and auditing in public cloud storage. Besides, an OMKHE technique is derived to accomplish data integrating into the cloud environment by the design of end to end encryption system. A secure generation of keys and encryption processes are carried out by the use of MKHE technique; thereby the data becomes highly secure. In addition, the choice of keys is performed by the improved beetle antenna search optimization (IBAS) algorithm. Therefore, the proposed BEA‐OMKHE technique offers an efficient way of enhancing the data integrity in CC method. The performance validation of the BEA‐OMKHE technique takes place and the results are inspected under various aspects. The comparative result analysis ensured the betterment of the BEA‐OMKHE technique interms of different measures such as communication cost, encryption time, decryption time, computation cost, privacy preserving rate, and authentication accuracy.

Wearable-User Authentication via Cross-Technology Interference in Heterogeneous Environments

The increasing deployment of wireless sensors enables a broad spectrum of health-related wearable applications. Due to the sensitivity of collected personal health information, these wearables should be authenticated together with their users as “wearable-user pairs” to ensure that they are attached to legitimate users. However, various devices are equipped with dedicated sensing abilities and wireless protocols corresponding to data characteristics in practice. Traditional authentication methodologies may not work in this heterogeneous environment because of protocol incompatibility. For example, how to verify a new ZigBee-enabled monitor when the existing trusted device is Wi-Fi-enabled? Therefore, to achieve authentication across protocols, in this article, we leverage the unique cross-technology interference (CTI), triggered by heterogeneous wireless transmissions, along with human physiological activity measurements (e.g., respiration patterns) to design an authentication scheme between wearables and users. Specifically, the authentication from an unknown ZigBee wearable to a trusted Wi-Fi device is achieved by monitoring the channel state information (CSI) changes according to human respiration. Our approach not only successfully recognizes a legitimate wearable-user pair but also blocks illegal access from adversaries. Extensive experiments have been conducted to demonstrate both the security and feasibility of the proposed scheme. The designed mechanism can achieve over 92% authentication accuracy with human subjects.

An IoT-Oriented Privacy-Preserving Fingerprint Authentication System

Identity authentication has become an essential component for access control in the Internet of Things (IoT) environment. To overcome the inherent weakness of password-based authentication, many present IoT devices (e.g., commercial banking smart cards) are equipped with the fingerprint authentication mechanism. However, due to the resource constraints of IoT devices, oversimplified authentication schemes are deployed, which compromise system performance significantly. Moreover, fingerprint templates in these existing schemes are unprotected. To address these issues, we propose an IoT-oriented privacy-preserving fingerprint authentication system. The proposed system is composed of four main components: 1) minutiae extraction; 2) the minutia cylinder-code (MCC)-based cancelable binary template, generated by the proposed normalized random projection; 3) the lightweight, privacy-preserving template, built by novel pairwise Boolean operations; and 4) fingerprint matching. Our system can effectively mitigate preimage and hill-climbing attacks. A prototype of the proposed system is developed using a popular open-source platform (i.e., Open Virtual Platforms). Comprehensive experimental results on eight benchmark data sets validate the effectiveness of the proposed IoT-oriented fingerprint authentication system. Our system also achieves equivalent authentication accuracy to that of the unprotected fingerprint authentication systems deployed in the resource-rich, non-IoT environment. More importantly, our system prototype is deployable to commercially available low-cost smart cards, such as Atmel AT24C256C Memory Smart Card 256K Bits. To the best of our knowledge, the proposed system is the first privacy-preserving, cancelable fingerprint authentication system developed in such a resource-constrained IoT setting.

I Want to Know Your Hand

We present HoldPass, the first system that can authenticate a user while they simply hold their phone. It uses the heart activity as biometric trait sensed via the hand vibrations in response to the cardiac cycle - a process known as ballistocardiography (BCG). While heart activity has been used for biometric authentication, sensing it through hand-based ballistocardiography (Hand-BCG) using standard sensors found on commodity mobile phones is an uncharted territory. Using a combination of in-depth qualitative analysis and large-scale quantitative analysis involving over 100 volunteers, we paint a detailed picture of opportunities and challenges. Authentication based on Hand-BCG is shown to be feasible but the signal is weak, uniquely prone to motion artifacts and does not land itself to the common approach of alignment-based authentication. HoldPass addresses these challenges by introducing a novel alignment-free authentication scheme that builds on asynchronous signal slicing and a data-driven algorithm for identifying a reduced set of features for characterizing a user. We implement HoldPass and evaluate it using a multi-modal approach: a large-case study involving 112 volunteers and targeted studies with a smaller set of volunteers over a period of several months. The data shows that HoldPass provides an authentication accuracy and user experience on par with or better than state-of-the-art systems with stronger requirements on hardware and/or user participation.

Threshold-free multi-attributes physical layer authentication based on expectation–conditional maximization channel estimation in Internet of Things

With the number of Internet of Things devices continually increasing, the endogenous security of Internet of Things communication systems is growingly critical. Physical layer authentication is a powerful means of resisting active attacks by exploiting the unique characteristics inherent in wireless signals and physical devices. Many existing physical layer authentication schemes usually assume physical layer attributes obey certain statistical distributions that are unknown to receivers. To overcome the uncertainty, machine learning–based authentication approaches have been employed to implement threshold-free authentication. In this article, we utilize an expectation–conditional maximization algorithm to provide the physical layer attribute estimates required for the authentication phase and a logistic regression model to achieve threshold-free physical layer authentication. Moreover, a Frank–Wolfe algorithm is considered to achieve fast convergence of the logistic regression parameters and multi-attributes are adopted to increase the differentiation of transmitters. Simulation results demonstrate that the obtained attribute estimates are sufficient to provide a reliable source of data for authentication and the proposed threshold-free multi-attributes physical layer authentication scheme can effectively improve authentication accuracy, with the false alarm rate P f reduced to 0.0263% and the miss detection rate P m reduced to 0.3466%.

Biometrics: Trust, But Verify

Over the past two decades, biometric recognition has exploded into a plethora of different applications around the globe. This proliferation can be attributed to the high levels of authentication accuracy and user convenience that biometric recognition systems afford end-users. However, in-spite of the success of biometric recognition systems, there are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems that create an element of mistrust in their use - both by the scientific community and also the public at large. Some of these problems include: i) questions related to system recognition performance, ii) security (spoof attacks, adversarial attacks, template reconstruction attacks and demographic information leakage), iii) uncertainty over the bias and fairness of the systems to all users, iv) explainability of the seemingly black-box decisions made by most recognition systems, and v) concerns over data centralization and user privacy. In this paper, we provide an overview of each of the aforementioned open-ended challenges. We survey work that has been conducted to address each of these concerns and highlight the issues requiring further attention. Finally, we provide insights into how the biometric community can address core biometric recognition systems design issues to better instill trust, fairness, and security for all.

EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey

An electroencephalogram (EEG) is a measurement that reflects the overall electrical activity in the brain. EEG signals are effective for biometric authentication and robust against malware attacks and any kind of fraud activities due to the uniqueness of the signals. Significant progress in research on EEG-based authentication has been achieved in the last few years, with machine learning being extensively used for classifying EEG signals. However, to the best of our knowledge, there has been no investigation into the overall progress made in such research. In this paper, the literature on the various factors involved in state-of-the-art biometric authentication systems is reviewed. We provide a thorough comparison of different machine learning biometric authentication techniques. The comparison criteria include the research objectives, machine learning algorithms, computational complexity, source of brainwaves, feature extraction methods, number of channels, and so on. Alongside the discussion of existing works, directions for future research are suggested to improve authentication accuracy. This paper provides an in-depth discussion of different advanced biometric authentication techniques, and a vivid picture of state-of-the-art machine learning-based biometric authentication techniques using EEG.