31 research outputs found

    Keystroke dynamics in the pre-touchscreen era

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    Biometric authentication seeks to measure an individualā€™s unique physiological attributes for the purpose of identity verification. Conventionally, this task has been realized via analyses of fingerprints or signature iris patterns. However, whilst such methods effectively offer a superior security protocol compared with password-based approaches for example, their substantial infrastructure costs, and intrusive nature, make them undesirable and indeed impractical for many scenarios. An alternative approach seeks to develop similarly robust screening protocols through analysis of typing patterns, formally known as keystroke dynamics. Here, keystroke analysis methodologies can utilize multiple variables, and a range of mathematical techniques, in order to extract individualsā€™ typing signatures. Such variables may include measurement of the period between key presses, and/or releases, or even key-strike pressures. Statistical methods, neural networks, and fuzzy logic have often formed the basis for quantitative analysis on the data gathered, typically from conventional computer keyboards. Extension to more recent technologies such as numerical keypads and touch-screen devices is in its infancy, but obviously important as such devices grow in popularity. Here, we review the state of knowledge pertaining to authentication via conventional keyboards with a view toward indicating how this platform of knowledge can be exploited and extended into the newly emergent type-based technological contexts

    Recognizing Human Affection: Smartphone Perspective

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    Touch-screen Smartphone has become an obligatory segment in the lives of billions of people around the world. Understanding the human affection or emotional state of the user enables efficient human computer interaction. Smartphone is one of the most frequently used electronic devices and the number of applications developed for it is increasing day by day. Emotion recognition of the user will lead to the development of emotion aware applications. Service recommendations and intelligent user interfaces in Smartphone will be other encouraging scopes for the mobile application developers. In this paper we discuss about state-of-the-art technologies to detect human emotional states. We proposed a methodology by which three different emotional states (positive, neutral, negative) of the user can be identified using Smartphone2019;s built-in sensors like the gyroscope, accelerometer and also additional sensors such as pressure sensor. We tried to analyse infraction log of Smartphone users, approximated different sensor values to recognize human emotions. Since the pressure values found on the existing phones are not completely accurate, we introduced the use of Force Sensitive Resistor (FSR) sensor to get more accurate pressure values

    Improving the performance of free-text keystroke dynamics authentication by fusion

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    Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00% False Accept Rate (FAR) and 0.00% False Reject Rate (FRR)

    Credential hardening by using touchstroke dynamics

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    Today, reliance on digital devices for daily routines has been shifted towards portable mobile devices. Therefore, the need for security enhancements within this platform is imminent. Numerous research works have been performed on strengthening password authentication by using keystroke dynamics biometrics, which involve computer keyboards and cellular phones as input devices. Nevertheless, experiments performed specifically on touch screen devices are relatively lacking. This paper describes a novel technique to strengthen security authentication systems on touch screen devices via a new sub variant behavioural biometrics called touchstroke dynamics. We capitalize on the high resolution timing latency and the pressure information on touch screen panel as feature data. Following this a light weight algorithm is introduced to calculate the similarity between feature vectors. In addition, a fusion approach is proposed to enhance the overall performance of the system to an equal error rate of 7.71% (short input) and 6.27% (long input)

    A Review of Emotion Recognition Methods from Keystroke, Mouse, and Touchscreen Dynamics

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    Emotion can be defined as a subjectā€™s organismic response to an external or internal stimulus event. The responses could be reflected in pattern changes of the subjectā€™s facial expression, gesture, gait, eye-movement, physiological signals, speech and voice, keystroke, and mouse dynamics, etc. This suggests that on the one hand emotions can be measured/recognized from the responses, and on the other hand they can be facilitated/regulated by external stimulus events, situation changes or internal motivation changes. It is well-known that emotion has a close relationship with both physical and mental health, usually affecting an individualā€™s and a teamā€™s work performance, thus emotion recognition is an important prerequisite for emotion regulation towards better emotional states and work performance. The primary problem in emotion recognition is how to recognize a subjectā€™s emotional states easily and accurately. Currently, there are a body of good research on emotion recognition from facial expression, gesture, gait, eye-tracking, and other physiological signals such as speech and voice, but they are all intrusive and obtrusive to some extent. In contrast, keystroke, mouse and touchscreen (KMT) dynamics data can be collected non-intrusively and unobtrusively as secondary data responding to primary physical actions, thus, this paper aims to review the state-of-the-art research on emotion recognition from KMT dynamics and to identify key research challenges, opportunities and a future research roadmap for referencing. In addition, this paper answers the following six research questions (RQs): (1) what are the commonly used emotion elicitation methods and databases for emotion recognition? (2) which emotions could be recognized from KMT dynamics? (3) what key features are most appropriate for recognizing different specific emotions? (4) which classification methods are most effective for specific emotions? (5) what are the application trends of emotion recognition from KMT dynamics? (6) which application contexts are of greatest concern

    Free-text keystroke dynamics authentication with a reduced need for training and language independency

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    This research aims to overcome the drawback of the large amount of training data required for free-text keystroke dynamics authentication. A new key-pairing method, which is based on the keyboardā€™s key-layout, has been suggested to achieve that. The method extracts several timing features from specific key-pairs. The level of similarity between a userā€™s profile data and his or her test data is then used to decide whether the test data was provided by the genuine user. The key-pairing technique was developed to use the smallest amount of training data in the best way possible which reduces the requirement for typing long text in the training stage. In addition, non-conventional features were also defined and extracted from the input stream typed by the user in order to understand more of the users typing behaviours. This helps the system to assemble a better idea about the userā€™s identity from the smallest amount of training data. Non-conventional features compute the average of users performing certain actions when typing a whole piece of text. Results were obtained from the tests conducted on each of the key-pair timing features and the non-conventional features, separately. An FAR of 0.013, 0.0104 and an FRR of 0.384, 0.25 were produced by the timing features and non-conventional features, respectively. Moreover, the fusion of these two feature sets was utilized to enhance the error rates. The feature-level fusion thrived to reduce the error rates to an FAR of 0.00896 and an FRR of 0.215 whilst decision-level fusion succeeded in achieving zero FAR and FRR. In addition, keystroke dynamics research suffers from the fact that almost all text included in the studies is typed in English. Nevertheless, the key-pairing method has the advantage of being language-independent. This allows for it to be applied on text typed in other languages. In this research, the key-pairing method was applied to text in Arabic. The results produced from the test conducted on Arabic text were similar to those produced from English text. This proves the applicability of the key-pairing method on a language other than English even if that language has a completely different alphabet and characteristics. Moreover, experimenting with texts in English and Arabic produced results showing a direct relation between the usersā€™ familiarity with the language and the performance of the authentication system

    Hardware and User Profiling for Multi-factor Authentication

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    Most software applications rely on the use of user-name and passwords to authenticate end users. This form of authentication, although used ubiquitously, is widely considered unreliable due to the users inability to keep them secret; passwords being prone to dictionary or rainbow-table attacks; as well as the ease with which social engineering techniques can obtain passwords. This can be mitigated by combining a variety of diferent authentication mechanisms, for example biometric authentication such as fingerprint recognition or physical tokens such as smart cards. The resulting multifactor authentication is typically stronger than any of the techniques used individually. However, it may still be expensive or prohibited to implement and more dificult to deploy due to additional accessories cost, e.g, finger print reader. Multi-modal biometric systems are those which utilise or are capable of utilising, more than one physiological or behavioural characteristic for enrolment, verification, or identification. So, in this research we present a multi-factor authentication scheme that is based on the user's own hardware environment, e.g. laptop with fingerprint reader, thus avoiding the need of deploying tokens and readily available biometrics, e.g., user keystrokes. The aim is to improve the reliability of the authentication using a multi-factor approach without incurring additional cost or making the deployment of the solution overly complex. The presented approach in this research uses unique sequential hardware information available from the user's environment to profile user behaviour. This approach improves upon password mechanisms by introducing a novel Hardware Authentication and User Profiling (HAUP) in form of Multi-Factor Authentication MFA that can be easily integrated into the traditional authentication methods. In addition, this approach observes the advantage of the correlation between user behaviour and hardware environment as an implicit veri_cation identity procedure to discriminate username and password usage, in particular hardware environment by specific pattern. So, the proposed approach uses hardware information to profile the user's environment when user-name and password are typed as part of the log-in process. These Hardware Manufacture Serial Part Numbers (HMSPNs) profiles are then correlated with the users behaviour, e.g., key-stroke behaviour that allows the system to profile user's behaviour dependent on their environment. As a result of this approach, the access control system can determine a particular level of trust for each user and base access control decisions on it in order to reduce potential identity fraud

    Identifying emotional states through keystroke dynamics

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    The ability to recognize emotions is an important part of building intelligent computers. Extracting the emotional aspects of a situation could provide computers with a rich context to make appropriate decisions about how to interact with the user or adapt the system response. The problem that we address in this thesis is that the current methods of determining user emotion have two issues: the equipment that is required is expensive, and the majority of these sensors are invasive to the user. These problems limit the real-world applicability of existing emotion-sensing methods because the equipment costs limit the availability of the technology, and the obtrusive nature of the sensors are not realistic in typical home or office settings. Our solution is to determine user emotions by analyzing the rhythm of an individualā€˜s typing patterns on a standard keyboard. Our keystroke dynamics approach would allow for the uninfluenced determination of emotion using technology that is in widespread use today. We conducted a field study where participantsā€˜ keystrokes were collected in situ and their emotional states were recorded via self reports. Using various data mining techniques, we created models based on 15 different emotional states. With the results from our cross-validation, we identify our best-performing emotional state models as well as other emotional states that can be explored in future studies. We also provide a set of recommendations for future analysis on the existing data set as well as suggestions for future data collection and experimentation

    Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

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    This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective
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