40 research outputs found

    Strengthening e-banking security using keystroke dynamics

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    This paper investigates keystroke dynamics and its possible use as a tool to prevent or detect fraud in the banking industry. Given that banks are constantly on the lookout for improved methods to address the menace of fraud, the paper sets out to review keystroke dynamics, its advantages, disadvantages and potential for improving the security of e-banking systems. This paper evaluates keystroke dynamics suitability of use for enhancing security in the banking sector. Results from the literature review found that keystroke dynamics can offer impressive accuracy rates for user identification. Low costs of deployment and minimal change to users modus operandi make this technology an attractive investment for banks. The paper goes on to argue that although this behavioural biometric may not be suitable as a primary method of authentication, it can be used as a secondary or tertiary method to complement existing authentication systems

    Development of the Keystroke Dynamics Recognition System

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    The paper is related to creating an algorithm for keystroke dynamics recognition and development of software, which is able to identify users according to their keystroke dynamics. Different characteristics of keystroke dynamics are considered. Probabilistic-statistical methods are compared with neural network algorithms for recognition. The algorithm for recognition was created and implemented. The software was tested with the help of some users. Their keystroke dynamics was analyzed in order to determine an efficiency of the created algorithm

    Development of the keystroke dynamics recognition system

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    The paper is related to creating an algorithm for keystroke dynamics recognition and development of software, which is able to identify users according to their keystroke dynamics. Different characteristics of keystroke dynamics are considered. Probabilistic-statistical methods are compared with neural network algorithms for recognition. The algorithm for recognition was created and implemented. The software was tested with the help of some users. Their keystroke dynamics was analyzed in order to determine an efficiency of the created algorithm

    Critical success factors for preventing E-banking fraud

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    E-Banking fraud is an issue being experienced globally and is continuing to prove costly to both banks and customers. Frauds in e-banking services occur as a result of various compromises in security ranging from weak authentication systems to insufficient internal controls. Lack of research in this area is problematic for practitioners so there is need to conduct research to help improve security and prevent stakeholders from losing confidence in the system. The purpose of this paper is to understand factors that could be critical in strengthening fraud prevention systems in electronic banking. The paper reviews relevant literatures to help identify potential critical success factors of frauds prevention in e-banking. Our findings show that beyond technology, there are other factors that need to be considered such as internal controls, customer education and staff education etc. These findings will help assist banks and regulators with information on specific areas that should be addressed to build on their existing fraud prevention systems

    Towards Web-based Biometric Systems Using Personal Browsing Interests

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    International audienceWe investigate the potential to use browsing habits and browser history as a new authentication and identification system for the Web with potential applications to anomaly and fraud detection. For the first time, we provide an empirical analysis using data from 4,5784,578 users. We employ the traditional biometric analysis and show that the False Acceptance Rate can be low (FAR=1.1%FAR=1.1\%), though this results in a relatively high False Rejection Rate (FRR=13.8%FRR=13.8\%). The scheme may either be utilized by Web service providers (with access to user's browser history) or any Webmaster, using other specialized techniques such as timing-based browser cache sniffing or a browser extension. We construct such a proof-of-concept extension
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