Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

Traditional access control systems that rely on static or fixed-point authentication are considered insecure andx000D
not suitable for mission-critical systems, especially when the users share their credentials, even when thex000D
security policies in place forbid those behaviours. Without continually checking that "you are who you claimx000D
you are", a potential security breach is almost certain to happen at some point. To solve this challenges withx000D
HCI touch screen data our approach will focus on investigating the available HCI touch screen datasets andx000D
validate their fitness for identity recognition and verification. After studying the touchscreen interaction data,x000D
we will focus on engineering relevant features to describe the unique behaviours of the user while interactingx000D
with touchscreen. We will investigate different feature selection and extraction techniques to identify the bestx000D
features for identity verification. The next stage will focus on evaluating pattern classification algorithms thatx000D
require positive training only (e.g. artificial immune system) to identify the most promising algorithm forx000D
novelty detection. Finally, we will propose an architecture for a non-intrusive continuous authentication. Thex000D
expected outcome of the project includes the following:x000D
1. Evaluate the effectiveness of using touchscreen data for continuous authentication.x000D
2. Engineering features for pattern classifications from touchscreen interaction datax000D
3. Design a new non-intrusive continuous authentication model based on touch screen data.