Ease of use and usefulness are believed to be fundamental in determining the acceptance and use of various, corporate ITs. These beliefs, however, may not explain the user’s behavior toward newly emerging ITs, such as the World-Wide-Web (WWW). In this study, we introduce playfulness as a new factor that reflects the user’s intrinsic belief in WWW acceptance. Using it as an intrinsic motivation factor, we extend and empirically validate the Technology Acceptance Model (TAM) for the WWW context. # 2001 Elsevier Science B.V. All rights reserved.

/pdf/beyond-the-usefulness-and-ease-of-use-extending-the-tam-for-2r69uxvmta.pdf

Beyond the Usefulness and Ease of Use : Extending the TAM for a World - Wide - Web Context

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

/pdf/hmog-new-behavioral-biometric-features-for-continuous-wxqxi7t4mv.pdf

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

Give us 5 minutes and we will show you the best book to read today. This is it, the handbook of data mining and knowledge discovery that will be your best choice for better reading book. Your five times will not spend wasted by reading this website. You can take the book as a source to make better concept. Referring the books that can be situated with your needs is sometime difficult. But here, this is so easy. You can find the best thing of book that you can read.

Handbook of Data Mining and Knowledge Discovery

Smartphones and tablets have become ubiquitous in our daily lives. Smartphones, in particular, have become more than personal assistants. These devices have provided new avenues for consumers to play, work, and socialize whenever and wherever they want. Smartphones are small in size, so they are easy to handle and to stow and carry in users’ pockets or purses. However, mobile devices are also susceptible to various problems. One of the greatest concerns is the possibility of breach in security and privacy if the device is seized by an outside party. It is possible that threats can come from friends as well as strangers. Due to the size of smart devices, they can be easily lost and may expose details of users’ private lives. In addition, this might enable pervasive observation or imitation of one’s movements and activities, such as sending messages to contacts, accessing private communication, shopping with a credit card, and relaying information about where one has been. This paper highlights the potential risks that occur when smartphones are stolen or seized, discusses the concept of continuous authentication, and analyzes current approaches and mechanisms of behavioral biometrics with respect to methodology, associated datasets and evaluation approaches.

Authentication of Smartphone Users Using Behavioral Biometrics

https://www.research.manchester.ac.uk/portal/files/33082822/COSE_Preprint_escholar.pdf

A survey on touch dynamics authentication in mobile devices

Security and protection of personal data are becoming more and more important. At the same time, we see a steady rise of very powerful mobile devices like smartphones and tablets. These devices offer most capabilities of desktop computers. Even today, they are often used for storing or accessing individual data. In the near future, it is obviously desirable to use their Internet capability to access private data or even business intra-nets from everywhere. Therefore, new security mechanisms must be designed. In this paper, we only focus on the authentication part (and ignore OS and network issues). Traditional methods for authentication (like smartcards or face recognition) may not be used in every context because of hardware and environment limitations (dark environments or camera restrictions in business). Therefore, we suggest to develop a mixture of a keystroke-based and a handwriting-based authentication method using capacitive displays. In this paper, we will briefly discuss limitations of existing approaches and why we believe that keystroke and handwriting authentication is a possible way for improving the security on mobile devices. First experiments will demonstrate the effectivity of this new approach with error rates under 2%.

Toward Mobile Authentication with Keystroke Dynamics on Mobile Phones and Tablets

Security through biometric keystroke authentication on mobile phones with a capacitive display and a QWERTZ-layout is a new approach. Keystroke on mobile phones with a 12 -key layout has already shown the possibility for authentication on these devices. But with hardwar e changes, new general requirements have been arisen. In this paper, we focus on the authentication with keystroke dynamics. Therefore, we are presenting new implemented keyboard layouts to show differences between a 12 -key layout and a QWERTZ -layout. In addition, we compare a numerical (PIN) and alphabetic (password) input for mobile phones. For this, we added new features for a keystroke authentication with a capacitive display. With the knowledge of the fault rates, we discuss the improvement of the se curity for keystroke dynamics with different virtual keyboard layouts. Our results show, even with new hardware factors, that an authentication via keystroke dynamics is possible.

/pdf/biometric-authentication-through-a-virtual-keyboard-for-7ia0x3uuur.pdf

Biometric authentication through a virtual keyboard for smartphones

Computational social sensing is enabled by the Internet of Things at large scale. Using sensors, e. g., implemented in mobile and wearable devices, human behavior and activities can then be investigated, e.g., using according models and patterns. However, the obtained models are often not explicative, i. e., interpretable, transparent, and explanation-aware, which makes assessment and validation difficult for humans. This paper proposes a novel explicative classification approach featuring interpretable and explainable models. For this purpose, we embed a framework for building rule-based classifiers using class association rules. For evaluation, we apply two real-world datasets: One collected in the domain of personalized health using wearable sensors (accelerometers), the second one utilizing smartphone sensors for activity recognition. Our results indicate, that the proposed approach outperforms the baselines clearly, concerning both accuracy and complexity of the resulting predictive models.

Explicative human activity recognition using adaptive association rule-based classification

Automatic emotion recognition through computers offers a lot of advantages, as the interaction between human and computers can be improved. For example, it is possible to be responsive to anger or frustration of customers automatically while working with a webpage. Mouse cursor movements and keystroke dynamics were already used and examined for such a recognition on conventional keyboards. The aim of this work is to investigate keystroke dynamics on touchscreen keyboards which gets a cumulative relevance through the increasingly further circulation of smartphones and tablets. Furthermore, it is possible to record additional information like pressure and size of keystrokes. This could increase the recognition rate for emotions. In order to record the keystroke dynamics, an application and keyboard layout for Android OS were developed. In addition, hypotheses were established on the basis of Yerkes-Dodson-Law and Flow theory and besides, a study with 152 test persons for the data collection was implemented. Subsequently, a data evaluation with the SPSS software was accomplished. Most of the hypotheses were confirmed and the results of the study show that emotions can be explained by the keystroke dynamics and recognized in this way.

Emotion Recognition through Keystroke Dynamics on Touchscreen Keyboards

The analysis of human activity data is an important research area in the context of ubiquitous and social environments. Using sensor data obtained by mobile devices, e. g., utilizing accelerometer sensors contained in mobile phones, behavioral patterns and models can then be obtained. However, the utilized models are often not simple to interpret by humans in order to facilitate assessment, evaluation and validation, e. g., in computational social science or in medical contexts. In this paper, we propose a novel approach for generating interpretable rule sets for classification: We present an adaptive framework for mining class association rules using subgroup discovery, and analyze different techniques for obtaining the final classifier. The approach is investigated in the context of human activity recognition. For our evaluation, we apply real-world activity data collected using mobile phone sensors.

/pdf/adaptive-class-association-rule-mining-for-human-activity-4ocdnpaf9s.pdf

Matthias Trojahn

Papers

Toward Mobile Authentication with Keystroke Dynamics on Mobile Phones and Tablets

Biometric authentication through a virtual keyboard for smartphones

Explicative human activity recognition using adaptive association rule-based classification

Emotion Recognition through Keystroke Dynamics on Touchscreen Keyboards

Adaptive class association rule mining for human activity recognition