#RADBEACON MCDONALDS VERIFICATION#
The user verification process employs four different one-class classifiers (OCCs), which is evaluated on the collected dataset using the holdout test method. We design and develop an Android-based prototype application as a proof-of-concept and collect a new dataset consisting of 21263 observations from 41 users in a real scenario. IDeAuth verifies legitimate owners of their smartphones by exploiting their micro hand-movements and decides to sign off the default user account revoking security-sensitive applications and services linked with it. IDeAuth is an implicit deauthentication scheme that aims to minimize unauthorized access to security-sensitive applications and services running on users' smartphones when unauthorized access or intrusions are detected. Many studies have shown that single entry-point authentication schemes for smartphones can easily be circumvented. If this McDonald’s app was triggered by a beacon at the door, identified by 34A5E9EF-7E09-4BCE-8D5281357FA57AF3, 36374, 40 at 12:05 pm, and then was triggered by another beacon at the point of sale, identified by 34A5E9EF-7E09-4BCE-8D52-81357FA57AF3, 36374, 20 at 12:15pm, you could infer that it had taken a customer 10 minutes to be served by. Furthermore, we demonstrate that BLUFADE effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements. We show that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. The former was used to train and evaluate the deauthentication system performances, the latter to assess the privacy and to increase variance in training data. To assess BLUFADE‘s practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection continuously. In this work, we propose BLUFADE, a fast, secure, and transparent de-authentication system that takes advantage of blurred faces to preserve user privacy. For instance, although facial recognition-based methods might be a good fit for security and usability, they violate user privacy by constantly recording the user and the surrounding environment. Unfortunately, no single approach offers security, privacy, and usability. To mitigate this threat, the research community focused on automated de-authentication systems. Travel Virgin Atlantic is using iBeacons at Heathrow to provide personalised services and offers to Upper Class passengers, and envisage further customer service oriented applications.
Dangling or unattended sessions expose users to well-known Lunchtime Attacks. McDonalds restaurants in Germany are using iBeacons in conjunction with QR codes and Apple's Passbook to present visitors with offers. While the user must pass the former to start a secure session, the latter’s importance is often ignored or underestimated. Ideally, secure user sessions should start and end with authentication and de-authentication phases, respectively.
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