SEVERITY OF VULNERABILITIES IN FEATURES AND ITS COUNTERMEASURES TO ENSURE DATA SECURITY AND PERSONALIZED DATA SAFETY IN AN AUTONOMOUS DRIVING WEB APPLICATION WITH CLOUD STORAGE IN THE EU - Brynner, Parir Elvis (Universiti Teknologi PETRONAS, 2021)
An autonomous driving application, Risk Estimation with a Learning AI (RELAI) is a web application by Engineering Data Intelligence GmbH that is directly connected to a cloud system. The aim of the autonomous web application is to derive new variant-rich synthetic test scenarios through learning methods where models are developed and trained that generalise test scenarios. Nowadays, almost every software application development has been using cloud system support to improve their performance. Centralised databases have provided the websites the ease of retrieving necessary data from cloud services as well as storing sensitive information. Availability of data in the cloud for the web application is beneficial for many web applications but it poses risks by exposing data (especially personalised data) to web applications which might already have security loopholes in them. Similarly, use of virtualization for cloud system services might risk data when user runs the software application without knowing the reliability of the user’s interaction with the software application.