Research on the process of writing uses bursts and pauses as key artifacts of underlying cognitive processes. However, the definition of a pause in writing is primarily based on tradition and ease of comparison between studies. This study explores keystroke logging data collected from middle school pupils (N=46) in northern Sweden, Norway and Finland and questions the traditionally defined pause’s usefulness, comparability and validity for investigating the underlying cognitive processes during writing. By examining the raw computer keystroke log data it was revealed that the group had a large variance in typing speed between participants and that different textual contexts had big variances compared to each other. Through exploration of different pause definitions’ effects on the text it was concluded that the twice the median length of pause (median x 2) was a good measurement for investigating pauses in sentences. Further, the 1.5 times the median (median x 1.5) for pauses between keystrokes within words proved useful for investigating the production of individual words. / Forskning på skrivprocesser har länge använt kaskader och pauser som nyckelartefakter av underliggande kognitiva processer. Definitionen av en paus i skrivande är dock främst baserade på tradition och direkt jämförbarhet mellan studier. Denna studie utforskar loggade tangenttrycknings data insamlade från elever (N=46) i mellanstadiet i skolor i norra Sverige, Norge och Finland och ifrågasätter den traditionellt definerade pausens användbarhet, jämförbarhet och validitet för att utforska underliggande kognitiva processer under skrivande. Genom att granska rå-data visade det sig att gruppen som helhet hade stora skillnader i skrivhastighet mellan deltagarna och att olika textuella kontext hade stora skillnader jämfört med varandra. Genom en undersökning av olika paus definitioners effekter på text kom det fram att dubbla längden på medianen för pauser (medianen x 2) var ett bra mått för att undersöka pauser i meningar. Dessutom var 1.5 gånger medianen (medianen x 1.5) i inom-ords kontexter ett användbart mått för att studera produktionen av individuella ord.

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 individuals 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 participants 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.

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 individuals 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 participants 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.

No description available.

In the traditional login systems, we use the username and the password to identify the legalities of users. It is a simple and convenient way to identify, but passwords could be stolen or copied by someone who tries to invade the system illegally. Adding one protective mechanism to identify users, the way of biometrics are brought out, such as keystroke dynamics, fingerprints, DNA, retinas and so on that are unique characteristics of each individuals, it could be more effective in preventing trespassing. This thesis uses keystroke biometrics as research aspects of user authentication. The advantages of this system are low-cost and high security to identify users using keyboard to calculate the time of keystrokes. In this thesis, we use statistical way to examine the researches and experiments. Chosen length of the username and password are greater than or equal to 9 characters, and learning sample sizes are 20 and adapting the sample adaptation mechanism, the results show that we achieved by False Acceptance Rate of 0.85%, False Rejection Rate of 1.51% and Average False Rate of 1.18%; all reach the high levels of safeties.

In login systems, a user is asked to enter his correct account and password in order to be allowed to enter to the system. The safety of systems is at the risk of leaking out the information, hence, the single mechanism of identity verification has not filled the bill at present. We study the personal typing behavior to get one¡¦s own specific features. In our thesis , we compare three methods and anlysis the advantages and shortcomings of those three. First one is to sort the twenty study data, and distribute the weights into the proper region. If the total weights is less than the threshold then this test data will be accepted, otherwise, it will be rejected. The second and third method are similar. Both of them are trying to rescale the data. The spirit of them is that the typing rate of a person will be faster when they type frequently and will be sloer when they are out of practice. However the relative positions of those keys, the lengths of ons¡¦s fingers, and the time that people making pauses in reading unpunctuated are unique. Those factors can be one¡¦s typing rhythm. There are twenty two individuals involved in this experiment. Each one choose his own proficient account and password to type and set up his typing model. The imposters are randomly choose legal user to imitate.

Background. Stress can profoundly affect human behavior. Critical-infrastructure operators (e.g., at nuclear power plants) may make more errors when overstressed; malicious insiders may experience stress while engaging in rogue behavior; and chronic stress has deleterious effects on mental and physical health. If stress could be detected unobtrusively, without requiring special equipment, remedies to these situations could be undertaken. In this study a common computer keyboard and everyday typing are the primary instruments for detecting stress. Aim. The goal of this dissertation is to detect stress via keystroke dynamics – the analysis of a user’s typing rhythms – and to detect the changes to those rhythms concomitant with stress. Additionally, we pinpoint markers for stress (e.g., a 10% increase in typing speed), analogous to the antigens used as markers for blood type. We seek markers that are universal across all typists, as well as markers that apply only to groups or clusters of typists, or even only to individual typists. Data. Five types of data were collected from 116 subjects: (1) demographic data, which can reveal factors (e.g., gender) that influence subjects’ reactions to stress; (2) psychological data, which capture a subject’s general susceptibility to stress and anxiety, as well as his/her current stress state; (3) physiological data (e.g., heart-rate variability and blood pressure) that permit an objective and independent assessment of a subject’s stress level; (4) self-report data, consisting of subjective self-reports regarding the subject’s stress, anxiety, and workload levels; and (5) typing data from subjects, in both neutral and stressed states, measured in terms of keystroke timings – hold and latency times – and typographical errors. Differences in typing rhythms between neutral and stressed states were examined to seek specific markers for stress. Method. An ABA, single-subject design was used, in which subjects act as their own controls. Each subject provided 80 typing samples in each of three conditions: (A) baseline/neutral, (B) induced stress, and (A) post-stress return/recovery-to-baseline. Physiological measures were analyzed to ascertain the subject’s stress level when providing each sample. Typing data were analyzed, using a variety of statistical and machine learning techniques, to elucidate markers of stress. Clustering techniques (e.g., K-means) were also employed to detect groups of users whose responses to stress are similar. Results. Our stressor paradigm was effective for all 116 subjects, as confirmed through analysis of physiological and self-report data. We were able to identify markers for stress within each subject; i.e., we can discriminate between neutral and stressed typing when examining any subject individually. However, despite our best attempts, and the use of state-of-the-art machine learning techniques, we were not able to identify universal markers for stress, across subjects, nor were we able to identify clusters of subjects whose stress responses were similar. Subjects’ stress responses, in typing data, appear to be highly individualized. Consequently, effective deployment in a realworld environment may require an approach similar to that taken in personalized medicine.

This study examined the potential use of keystroke dynamics to create keyprints (typing fingerprints) to authenticate individuals in online assessment situations. The implications of this study are best understood in terms of the keystroke behavioral biometric. While previous studies considered the degree to which keystroke typing patterns are unique, this study was set up to determine how well keyprints are able to identify individuals when typing under various treatment conditions (copy typing, free typing, and typing with mild or moderate impediments). While authentication can be difficult when attempting to correctly identify individual users, the results of this study indicate that keyprints can be a solid indicator of negative cases (i.e., flagging situations where a typing sample is likely not the correct individual). As anticipated, typing with a temporary impediment does diminish the algorithms' ability to identify students. This is also the case when user samples are typed under conditions different from those in which the keyprint baseline signature was captured (i.e., copy versus free typing). The ability to identify individuals is also challenging when using small comparison samples. However, the ability of the system to identify negative cases functions fairly well in each instance.

<p>Biometri är en relativt ny säkerhetsmetod för datorsystem. Biometri används ofta för att ersätta eller kombineras med användarnamn och lösenord. Detta görs genom att mäta ett fysiologiskt attribut eller beteendeattribut hos användaren. Keystroke dynamics är en biometrisk metod vilken registrerar användarens sätt att skriva på tangentbordet. En stor mängd försök med keystroke dynamics har gjorts i tidigare arbeten. Många av dessa har utgått ifrån metoder vilka använder ett högt antal stickprov från användarens beteende vid tangentbordet. Optimalt är dock en metod med hög säkerhet men samtidigt använder ett lågt antal stickprov. Denna rapport introducerar en ny algoritm för implementering av keystroke dynamics, vilken jämförs med två existerande algoritmer. Denna rapport visar att den nya algoritmen har högre prestanda än de övriga två i jämförelsen</p>

<p>One important issue faced by companies is to secure their information and resources from intrusions. For accessing a resource almost every system uses the approach of assigning a unique username and a password to all legitimate users. This approach has a major drawback. If an intruder gets the above information then he can become a big threat for the company and its resources. To strengthen the computer security there are several biometric methods for identity verification which are based on the human body’s unique characteristics and behavior including fingerprints, face recognition, retina scan and signatures. However most of these techniques are expensive and requires the installation of additional hardware. </p><p>This thesis focuses on keystroke dynamics as an identity verifier, which are based on the user’s unique habitual typing rhythm. This technique is not just looking for <i>what</i> the user types but also <i>how</i> he types. This method does not require additional hardware to be installed and are therefore rather inexpensive to implement. This thesis will discuss how identity verification through keystroke characteristics can be made, what have been done in this area and give advantages and disadvantages of the technique.</p>