Perceptions of risk and security concerns using biometric vs traditional authentication methods: a statistical case study

Smartphones are the world's most widely used personal computing devices. PINs and passcodes have long been the most popular authentication methods in smartphones and even in the pre-smartphone era. Due to the inconvenient nature of PINs and passcodes, a new biometric authentication method for smartphones was developed and has been gaining traction in terms of adoption, beginning with flagship devices and progressing to some mid-range devices. This article aims to investigate the factors influencing smartphone owners' acceptance of biometric authentication methods by developing a new model based on the Technology Acceptance Model (TAM). It also validates the data with survey data from 233 Indonesian smartphone owners via an online survey and analyzed it using Structural Equation Modeling (SEM). The results from the SEM analysis show that all nine hypotheses in the proposed model are supported. In other words, all six factors in the proposed model (i.e., attitude toward the use, perceived usefulness, perceived the ease of use, perceived enjoyment, perceived security, and social influence) have significant effects on the behavioral intention of adopting biometric authentication methods among smartphone owners. More specifically, the findings indicate that most Indonesian smartphone users have a favorable attitude toward biometric authentication, which is why they are willing to adopt it. Furthermore, it is discovered that the perceived usefulness of a biometric authentication method on smartphones outweighs its perceived ease of use. It reveals that the user's belief in the intrinsic value of biometric authentication methods in the form of perceived security outweighs both the internal user motivation of perceived enjoyment and the external user motivation of social influence in terms of their acceptance of biometric authentication methods.

Nowadays, mobile smartphones are popular devices among general population. Compared to the traditional mobile phone, smartphone is closer to a personal computer with a good number of mobile applications installed. However, when a user browses certain mobile application, his or her account is at risks if static password is the only element required for authentication. To overcome this problem, multi-factor authentication is widely required for mobile applications. In this paper, we conduct a survey for a group of mobile application users and analyze the pros and cons of each authentication method by case studies. We focus on six main factors to evaluate each authentication method. Our study found that traditional password authentication, biometrics authentication methods such as fingerprint, face, and voice scored relatively high on Convenience, Time, Security and Accuracy. More important, these four aspects are exactly the most essential factors to a mobile application’s quality that relate to safety issue and user experience as well. Our evaluation results show that biometric authentication methods are currently used most often by mobile applications and well accepted by its users. Overall, we evaluated five cases with advantages and disadvantages. We found fingerprint, voice authentication played outstanding as biometric authentication methods. We also come up with a new authentication proposal for mobile application design and for the future research that in view of mobile devices are more and more functional nowadays, we recommend mobile applications to use biometric authentication in two steps authentication methods.

Traditional authentication methods use numbers or graphic passwords and thus involve the risk of loss or theft. Various studies are underway regarding biometric authentication because it uses the unique biometric data of a human being. Biometric authentication technology using ECG from biometric data involves signals that record electrical stimuli from the heart. It is difficult to manipulate and is advantageous in that it enables unrestrained measurements from sensors that are attached to the skin. This study is on biometric authentication methods using the neural network with weighted fuzzy membership functions (NEWFM). In the biometric authentication process, normalization and the ensemble average is applied during preprocessing, characteristics are extracted using Haar-wavelets, and a registration process called “training” is performed in the fuzzy neural network. In the experiment, biometric authentication was performed on 73 subjects in the Physionet Database. 10-40 ECG waveforms were tested for use in the registration process, and 15 ECG waveforms were deemed the appropriate number for registering ECG waveforms. 1 ECG waveforms were used during the authentication stage to conduct the biometric authentication test. Upon testing the proposed biometric authentication method based on 73 subjects from the Physionet Database, the TAR was 98.32% and FAR was 5.84%.

BackgroundThe Technology Adoption Model (TAM) offers a potential framework for elucidating the relationships between data privacy or security concerns and behavioral intention, perceived usefulness (PU), and perceived ease of use (PEOU) of mobile health (mHealth) apps, particularly for patients’ self-care management. In Saudi Arabia, limited information is available on these pertinent research areas despite the government’s relentless efforts to bolster the use of mHealth apps.ObjectiveThis study applies the TAM and the psychosociocultural framework to explore the influence of patients’ data privacy and security concerns on the PU, PEOU, and behavioral intention to use mHealth apps for self-care management in Saudi Arabia.MethodsA cross-sectional study was conducted by recruiting patients using mHealth apps for self-care from various provinces in Saudi Arabia. Research instruments were developed based on the components of 2 theories: the psychosociocultural framework and TAM, which were then piloted, validated, and distributed to participants via Google Forms. Linear regression models were performed to test the hypothesized relationships.ResultsOverall, 567 patients using mHealth apps participated in the study. Slightly more than one-third (217/567, 38.2%; range 35.6%‐41.4%) of the participants expressed a high level of concern regarding data privacy, confidentiality, and security, with significant predictors being female gender, higher educational qualifications, and younger age groups (<46 years). About 18% to 25% of the variance in PU, PEOU, and behavioral intention to use mHealth apps was explained by the tested factors. Patients were more likely to have higher PU following a unit decrease in data confidentiality (β=.31; P=.01) and security concerns (β=.47; P=.01). The PEOU of mHealth apps increased as users demonstrated less concern regarding data privacy (β=.18; P=.001), confidentiality (β=.24; P<.001), and security (β=.43; P=.02). Likewise, behavioral intention to use mHealth apps also increased significantly following a reduction in respondents’ concerns toward data privacy (β=.18; P=.02), confidentiality (β=.24; P=.03), and security issues (β=.36; P=.01).ConclusionsSpecific demographic factors and concerns regarding data security and privacy influence patients’ PU, PEOU, and behavioral intention to use mHealth apps for self-care management. Targeting the age-, education-, and gender-based differences regarding the usage of mHealth apps. Health care providers and policymakers may consider age-, education-, and gender-based differences when developing strategies to improve the adoption of mHealth apps among the Saudi patient population.

Application-Based Feeder Transportation (AFT) provides shared mobility that bridges last-mile gaps, promotes sustainable transit, and supports e-government initiatives. However, ridership remains far lower than that of privatevehicle trips, and research on societal acceptance of AFT is still limited, particularly in Indonesia. Therefore, the goal of this study is to investigate determinant behavior intention to use AFT, what factors influence their PU, and the part that social influence plays. The research does this by adding new constructs and linkages to the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). In July 2025, 210 users participated in our online survey using a Likert-scale that measured ten constructs. These constructs include Perceived Ease of Use (PEU), Perceived Usefulness (PU), Social Influence (SI), Perceived Risk (PR), Environmental Awareness (EA), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), Habit (HB), and Behavioral Intention (BI). Data analysis were carried out using partial least squares structural equation modeling (PLS-SEM). According to the findings, EA, SI, PU, and HB have a major beneficial impact on BI. PEU and SI have a major beneficial impact on PU, but PR has a detrimental one. SI exerts a major detrimental impact on PR and a beneficial impact on PEU and PU. However, PEU, PR, FC, HM, and PV showed minor impact on BI. This study extends the literature on acceptance of application-based feeder transportation and offers practical recommendations for AFT providers to enhance adoption.

Comparing fingerprint-based biometrics authentication versus traditional authentication methods for e-payment

The need of protection user biometric information increases in consequence of the development of biometric authentication systems. This is due to the fact that fingerprints, iris patterns, face geometry and other biometric data are unique and cannot be replaced. There are various methods for protecting biometric data, but the probability of compromise remains when this data is transmitted over the network. The article presents a method of remote biometric authentication using network steganography for different systems. This improves the reliability of the protection of user biometric data. Analysis of existing methods of network steganography, methods of biometric authentication. Synthesis of new method of remote authentication, that will increase the security of user biometric data from unauthorized access. Modeling a remote authentication system using network steganography methods and user biometric data is the method of this work. The common methods of biometric authentication, existing methods for their protection and existing network steganography methods were analyzed. The method of remote biometric authentication using network steganography for various systems is presented. The remote authentication system using network steganography was simulated. The resistance of the investigated methods to detection was evaluated. The effectiveness of the proposed method for protecting biometric data was investigated.

This study investigates the factors influencing the acceptance and use of digital banking services in Saudi Arabia, applying the technology acceptance model. Data were collected from 406 respondents using a structured survey to measure perceived usefulness (PU), perceived ease of use (PEOU), Trust, social influence (SI), perceived risk (PR), behavioral intention (BI), and actual usage. The results showed that PU, PEOU, Trust, SI, and BI significantly positively affected digital banking usage, while PR had a significant negative impact. The findings align with existing literature, reinforcing the importance of user-friendly interfaces, robust security measures, and SI in driving digital banking adoption. Practical implications include enhancing digital banking platforms' usability and trustworthiness to boost user engagement. Future research should consider longitudinal studies and explore the impact of emerging technologies on digital banking adoption. KEYWORDS digital transformation, adoption behavior, perceived usefulness (PU), perceived ease of use (PEOU)

Computers are depended on to store sensitive information and provide this information security from outsiders. Biometric authentication is very important in order to keep a system secure and keep information safe from outside attacks. Most of the biometric methods use expensive hardware and software in order to ensure this. Hence behavioural biometrics were proposed that perform authentication based on only behavioural characteristics of a user like gait, manner, way of typing. Keystroke dynamics offers great promise, emerging as one of the top ways for behavioural biometric authentication. Keystroke dynamics is extremely difficult to impersonate hence it is an extremely useful method for biometric authentication. In this paper, the best method for authentication using keystroke dynamics is discussed using the CMU Keystroke Dynamics Benchmark Data Set. Five Machine Learning Algorithms are compared to identify the best algorithm to be used to build the authentication system based on the lowest Equal Error Rate (EER).

With the exponential increase in the use of mobile devices across the globe, there is a concomitant need to understand how mobile users perceive the security of mobile application, and the potential risks involved in accessing and downloading them. Such an understanding will enable users to ensure the apps they download are secure and create greater awareness in the marketplace of the presence of hackers and malware used to invade the privacy and personal details of smartphones users. Research on the perception of users' mobile security is very limited and needs further investigation. This study aims to identify how mobile users perceive the security of different mobile apps and the extent to which different apps affect such perceptions. This study also investigates mobile user preferences for the places where they can access apps and their perceptions of risk at marketplaces vs. websites. This study is based on a qualitative research in which interviews were conducted with 32 university students. The study found that mobile users do not feel secure when installing mobile apps, and that concerns about hacking personal and private information are pervasive. Users expressed more security concerns regarding entertainment apps such as games and communication rather than financial apps, such as banking. The study also found that users prefer installing apps from app stores. The findings of this research contribute a greater understanding of how mobile users perceive mobile app security and offers insights that will help developers adjust their security policies to ensure users' security. The study also presents theoretical and empirical contributions, along with limitations and suggestions for further work.

Purpose – This study aims to determine the factors that affect the intention of Muslim students to adopt mobile banking (m-banking) services at Islamic banks. Methodology – This study included a sample of 336 Muslim students employed by Islamic banks as respondents. The model incorporates 11 constructs: perceived risk (PR), perceived trust (PT), habit (HA), compatibility (CO), perceived usefulness (PU), perceived ease of use (PEU), effort expectancy (EE), performance expectancy (PE), facilitating conditions (FC), social influence (SI), and intention to adopt m-banking (IN). It is a combination of the unified theory of acceptance and use of technology (UTAUT) and the technology acceptance model (TAM). Findings – Muslim students’ intention to adopt mobile banking was positively influenced by perceived usefulness, performance expectancy, and facilitating conditions. Nevertheless, the intention of Muslim students to adopt mobile banking remains unaffected by factors such as perceived ease of use, effort expectancy, or social influence. Perceived ease of use and perceived trust affect perceived usefulness, while perceived risk, perceived risk, habit, and compatibility affect perceived ease of use. Originality – This study employs the TAM-UTAUT model, which consists of 11 components, to determine the intention of Muslim students towards using mobile banking.

In order to meet loosely coupled, distributed authentication requirements and solve the diversity of identity providing and authentication methods in Service-Oriented Architecture, proposed a modified digital identity based authentication method that expands the identity metasystem authentication mechanism using WCF and WCS to provide a unified authentication and identity management method. Compared with traditional authentication methods, the new proposed method based on modified identity metasystem can provide security, extensible and loosely-coupled authentication method for Service-Oriented Architecture.

Purpose: Virtual Intelligent Sports Equipment (VISE) primarily refers to sports fitness mobile applications. Scholars utilize the Technology Acceptance Model (TAM) to explore the psychological factors underlying consumption behavior. However, existing research lacks sufficient attention to gender differences. Investigating gender differences is crucial for a deeper understanding of consumer psychology, facilitating the formulation of gender-specific marketing strategies. This study aimed to analyze the consumption psychology of VISE among college students, examining gender differences to provide insights for promoting high-quality development in the sports industry. Method: Undergraduate students from four universities in northwest China were surveyed, yielding 1,333 valid questionnaires (726 females, 607 males). The questionnaire utilized a 5-point Likert scale to assess Perceived Usefulness (PU), Perceived Ease of Use (PE), Perceived Playfulness (PP), Perceived Risk (PR), and Consumption Intention (CI). SPSS was employed for validity and reliability testing, as well as mean difference testing. AMOS was used for Confirmatory Factor Analysis and multiple-group structural equation modeling. Results: Full-sample analysis: The measurement model demonstrated good reliability and validity (Cronbach' α &gt; 0.9, CR &gt; 0.8, AVE &gt; 0.7), with satisfactory fit of the structural model (CMIN/DF = 4.174, GFI = 0.963, AGFI = 0.945, TLI = 0.983, CFI = 0.987, RMSEA = 0.049). In the multi-group analysis: among females, PR had no significant impact on CI (β = -0.017, P &gt; 0.05), while PE (β = 0.192), PU (β = 0.248), and PP (β = 0.432) all had a positive effect on CI (P &lt; 0.01), with the path coefficient from PE to CI being significantly higher than in males (Z = 2.577, P &lt; 0.05). Among males, PE had no significant impact on CI (β = 0.04, P &gt; 0.05), while PU (β = 0.28) and PP (β = 0.435) positively influenced CI (P &lt; 0.01), and PR had a negative impact on CI among males (β = -0.152, P &lt; 0.01), with the path coefficient significantly higher than in females (Z = 2.755, P &lt; 0.05). Mean comparisons: female averages for PP, PU, and CI were significantly higher than in males (P &lt; 0.01), while male averages for PR were significantly higher than in females (P &lt; 0.01). Conclusion: PP, PU, PE, and PR influence college students' VISE consumption intention. Factors influencing females' VISE consumption intention include PU, PP, and PE, while for males, it is PU, PP, and PR. Females prioritize device simplicity, while males prioritize device safety. Females demonstrate higher sensitivity to PP and PU compared to males, while males exhibit higher sensitivity to PR compared to females. The inclination of women towards VISE consumption is stronger than that of men, suggesting the need for device researchers to design with gender considerations in mind.

Objectives: User authentication is an indispensable element for secured network service. Due to the rapid advancement of Internetworking technologies, it is easy for attackers to access confidential data by compromising authentication methods. Traditional methods of biometric authentication have a weakness, especially in high-security systems because it gives chances for any attacker to obtain the system information. There is a strong desire to develop and implement more secure authentication method to protect such information against security threats. Methods: From the existing Biometric authentication methods, DNA (Deoxyribo Nucleic Acid) is said to be the best method due to its high accuracy and allows both identification and verification. At present DNA techniques are used mainly in Law enforcement, there is feasibility to extend this in real life system security. Findings: The proposed D-SSS is an innovative and new idea of DNA pattern extraction and pattern matching approach. Applications: The proposed (D-SSS) DNA-based biometric technique builds a new system of authentication that requires very less amount of saliva to protect the system efficiently with highlevel security. The D-SSS is a user-friendly approach, which uses the unique property that the user has at the time of authentication, will lead to intensive work in the area of system securityKeywords: Biometric system, DNA Extraction, Saliva, System Security

There are two types of personal authentication methods, that is, biometric authentication method and password authentication method. Although biometric authentication has an advantage that security is higher than password authentication, there is also a disadvantage that biometric information can not be replaced. On the other hand, an authentication method using human motion has been proposed in an existing research. Human motion is a kind of changeable biometric information even when biological information leaks out. In the authentication method, biometric information for authentication depends on an acceleration sensor and a gyro sensor. DP matching is used for the comparison of users in the research. However, false acceptance rate is 10.71% and false rejection rate is 56.90% in a single action. There is no way to use this method for personal identification. In our research, we also obtained data from acceleration sensors and gyroscopes which are standard equipments on usual smartphones. We used convolutional neural networks for the comparison of individuals instead of DP matching. As a result of personal identification by convolutional neural network with data of “circle”, “rectangle” and “check” which are acquired from examinees, individuals can be identified with a rate of more than 88.5% on average in multi-level classification and 78.2% on average in binary classification.