1,228 research outputs found

    Biometrics on mobile phone

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    Conceivable security risks and authentication techniques for smart devices

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    With the rapidly escalating use of smart devices and fraudulent transaction of users’ data from their devices, efficient and reliable techniques for authentication of the smart devices have become an obligatory issue. This paper reviews the security risks for mobile devices and studies several authentication techniques available for smart devices. The results from field studies enable a comparative evaluation of user-preferred authentication mechanisms and their opinions about reliability, biometric authentication and visual authentication techniques

    Users’ Attitudes on Mobile Devices: Can Users’ Practices Protect their Sensitive Data?

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    Smartphones are the most popular personal electronic devices. They are used for all sorts of purposes, from managing bank accounts to playing games. As smartphone apps and services proliferate, the amount of sensitive data stored on or processed by handheld devices rise as well. This practice entails risks, such as violating users’ privacy, stealing users’ identities, etc. Particularly, stealing an unlocked device grants full access to sensitive data and applications. In this survey, we examine whether users adopt some basic practices to protect their sensitive personal data themselves, or is there a need to further strengthen their protection? Our statistical analysis assesses smartphone users’ security attitudes and practices among different age groups. Finally, we investigate the factors that affect the attitude of users with respect to their practices for the protection of personal data.The results of this study, show that while many smartphone users do take some security precautions, a high percentage (24%) of them still ignores security and privacy risks. In addition, 19,1 % of users do not follow any practices to protect their PINs and Passwords

    Transparent authentication: Utilising heart rate for user authentication

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    There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

    IEDs on the Road to Fingerprint Authentication : Biometrics have vulnerabilities that PINs and passwords don't

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    Almost every 2016 flagship mobile phone, whether Android or iOS-based, is set to come with an integrated fingerprint reader. The convenience benefits of fingerprint readers are clear to users, but is the underlying technology really ready for widespread adoption? This article explores some of the background of the challenge of secure user authentication on mobile devices, as well as recent weaknesses identified in the handling of fingerprints on many consumer devices. It also considers legislatory and social implications of the widespread adoption of fingerprint authentication. Finally, it attempts to look forward to some resulting problems we may encounter in the future

    Behaviour Profiling for Transparent Authentication for Mobile Devices

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    Since the first handheld cellular phone was introduced in 1970s, the mobile phone has changed significantly both in terms of popularity and functionality. With more than 4.6 billion subscribers around the world, it has become a ubiquitous device in our daily life. Apart from the traditional telephony and text messaging services, people are enjoying a much wider range of mobile services over a variety of network connections in the form of mobile applications. Although a number of security mechanisms such as authentication, antivirus, and firewall applications are available, it is still difficult to keep up with various mobile threats (i.e. service fraud, mobile malware and SMS phishing); hence, additional security measures should be taken into consideration. This paper proposes a novel behaviour-based profiling technique by using a mobile user’s application usage to detect abnormal mobile activities. The experiment employed the MIT Reality dataset. For data processing purposes and also to maximise the number of participants, one month (24/10/2004-20/11/2004) of users’ application usage with a total number of 44,529 log entries was extracted from the original dataset. It was further divided to form three subsets: two intra-application datasets compiled with telephone and message data; and an inter-application dataset containing the rest of the mobile applications. Based upon the experiment plan, a user’s profile was built using either static and dynamic profiles and the best experimental results for the telephone, text message, and application-level applications were an EER (Equal Error Rate) of: 5.4%, 2.2% and 13.5% respectively. Whilst some users were difficult to classify, a significant proportion fell within the performance expectations of a behavioural biometric and therefore a behaviour profiling system on mobile devices is able to detect anomalies during the use of the mobile device. Incorporated within a wider authentication system, this biometric would enable transparent and continuous authentication of the user, thereby maximising user acceptance and security

    Towards a model of factors affecting resistance to using multi-method authentication systems in higher-education environments

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    Over the course of history, different means of object as well as person identification and verification have evolved for user authentication. In recent years, a new concern has emerged regarding the accuracy of authentication and of protection of personal identifying information (PII), because previous information systems (IS) misuses have resulted in significant financial loss. Such losses have escalated more noticeably because of identity-theft incidents due to breaches of PII within multiple public-access environments, such asinstitutions of higher-education. Although the use of various biometric and radio frequency identification (RFID) technologies is expanding, resistance to using these technologies remains an issue. As such, in this research-in-progress paper, we outline a predictive study to assess the contribution of campus students’ perceptions of the importance of protecting their PII, noted as Perceived Value of Organizational Protection of PII (PVOP), authentication complexity (AC), and invasion of privacy (IOP) on their resistance to using multi-method authentication systems (RMS) in higher-education environments. In this work-in-progress study, we seek to better understand the theoretical foundations for the effect of students’ perceptions on their resistance to using multi-method authentication systems (RMS) in higher-education environments and uncover key constructs that may significantly contribute to such resistance. A quasiexperiment is proposed including clearly identified procedures and data analyses

    Towards a development of a users’ ratified acceptance of multi-biometrics intentions model (RAMIM): Initial empirical results

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    User authentication is a continuous balance between the level of invasiveness and system security. Password protection has been the most widely user authentication approach used, however, it is easily compromised. Biometrics authentication devices have been implemented as less compromised approach. This paper reports on initial results of user perceptions about their acceptance of a multi-biometrics authentication approach in the context of elearning systems. Specifically, this paper reports on the initial empirical results on the development of a learners’ Ratified Acceptance of Multibiometrics Intentions Model (RAMIM). The model proposed look at the contributions of learners’ code of conduct awareness, perceived ease-of-use, perceived usefulness, and ethical decision making to their intention to use multi-biometrics for authentication during e-learning exams. The study participants included 97 managers from service oriented organization and government agencies who attended e-learning courses. Results demonstrated high reliability for all constructs measured and indicated that perceived easeof-use and perceived usefulness are significant contributors to learners’ intention to use multi-biometrics. Conversely, code of conduct awareness appears to have little or no contribution on learners’ intention to use multibiometrics, while learners’ ethical decision making appears to have marginal contribution
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