Identity Authentication Based on Keystroke Latencies Using Neural Networks

Most computer security systems verify the identity of a user through objects in the user\u27s possession such as keys or magnetic cards, or through knowledge the user has, such as a password or PIN number. There are, however, two other methods of user verification, which have as yet received little or no attention. There has been some work done on the third method, recognition of physiological patterns (such as finger prints, retinal patterns, or voice patterns), but this work has been limited and requires expensive hardware to implement. The final method of user verification is through actions such as signature or behavior patterns

ОГЛЯД ПІДХОДІВ К РОЗПІЗНАВАННЮ БІОМЕТРИЧНОГО ПОРТРЕТА КОРИСТУВАЧА ЗА КЛАВІАТУРНИМ ПОЧЕРКОМ

В роботі проаналізовані найбільш відомі роботи й алгоритми розпізнавання біометричного портрета користувача за клавіатурним почерком, їх переваги й недоліки. Показано, що жоден с підходів не забезпечує достовірне розпізнавання користувача

Deployment of Keystroke Analysis on a Smartphone

The current security on mobile devices is often limited to the Personal Identification Number (PIN), a secretknowledge based technique that has historically demonstrated to provide ineffective protection from misuse. Unfortunately, with the increasing capabilities of mobile devices, such as online banking and shopping, the need for more effective protection is imperative. This study proposes the use of two-factor authentication as an enhanced technique for authentication on a Smartphone. Through utilising secret-knowledge and keystroke analysis, it is proposed a stronger more robust mechanism will exist. Whilst keystroke analysis using mobile devices have been proven effective in experimental studies, these studies have only utilised the mobile device for capturing samples rather than the more computationally challenging task of performing the actual authentication. Given the limited processing capabilities of mobile devices, this study focuses upon deploying keystroke analysis to a mobile device utilising numerous pattern classifiers. Given the trade-off with computation versus performance, the results demonstrate that the statistical classifiers are the most effective

Počet měření pro vytvoření vzoru identifikačního pole dynamiky psaní krátkého textu na klávesnici

Keystoke dynamics is biometrics authentification. This biometrice usually does not involve some special hardware and it is advantage os this way how to prove you are really you. In this article it is suggested the criterium which determines how many measurement is necessary for creating template of keystroke dynamics. This criterium is simultaneously used in experiments

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development