8 research outputs found
Continuous authentication and its application in personal health record systems
Authenticating users in commercial smartphones is currently a naive process
putting the smartphone owner in security risks in events such as unauthorized device
sharing, device loss or theft, and session hijacking. With the recent interest of gov-
ernmental and health organizations to provide their users with applications that can
be run on their smartphones, securing these devices with measures above the cur-
rent solutions is imperative. In this research, we propose a continuous authentication
module for a Personal Health Record system that monitors its users for authenticity
over time via their touch biometrics and denies access to those who can not satisfy
authentication criteria.
The proposed solution can be used in any smartphone application that is highly
sensitive in terms of privacy and security which needs continuous authentication while
running. We will also propose a notification module that helps to build transparency
for the user about how their shared personal information is used in the system, so they
will be more willing to trust our application. The proposed continuous authentication
was implemented in an actual Personal Health Record system for Android enabled
smartphones to make it more secure and practical to use. The results show an average
precision of above 95% in detecting whether a user is the legit owner of a smartphone
or not. Finally, we composed an open-source dataset for touch biometrics and made
it available to the public. This is the first publicly available dataset related to touch
biometrics
Replication Data for: Continuous Authentication using Touch Dynamics and its Application in Personal Health Records
The dataset contains 20 data files for 20 participants with overall 125794 instances of touch dynamics information collected using TouchSense (available at https://play.google.com/store/apps/details?id=org.mun.navid.touchsens). The application is implemented in such a way that it prompts the user to type in 30 random words or numbers. While the user interacts with the keyboard, it captures the touch inputs corresponding to those actions and stores them in a data file. This dataset can be used exclusively for research purposes. Commercial purposes are fully excluded.
Attribute information: 1- pressure (numeric), 2- size (numeric), 3- touchmajor (numeric), 4- touchminor (numeric), 5- duration (numeric), 6- flytime (numeric), 7- shake (numeric), 8- orientation (numeric), 9- type (numeric), 10- class (AndroidId, Others)
Pressure: indicates the pressure applied by a touch action.
Size: indicates the number of pixels affected on the screen by a touch action.
Touch Major: reports the major axis of an ellipse that represents the touched area.
Touch Minor: reports the minor axis of an ellipse that represents the touched area.
Duration: represents the time interval from the moment a finger touches the screen until the finger loses contact with it.
Fly Time: shows the time elapsed between finishing typing a character and starting to type the next one.
Shake: records the amount of vibration of the smartphone while performing touch actions.
Orientation: records whether the touch behavior was recorded while the device was in the landscape orientation or the portrait one.
Word or Number: records whether the touch behavior involves typing in a word or a number
Decision tree generated by C&RT model run on dataset with feature selection filtering.
<p>This model suggests that the following 3 combination of features (routes) can result in high maize grain yield: (1) Sowing date and country in [âAUS-North-ten Mayâ âBA-South-fifteen Octâ âBI-North-eleven Mayâ âPA-South-mid Sepâ âSh-North-fourteen Juneâ] and KNPE >426 and Stem dry weight >122.478 and Mean KW >196.4 mg, (2) Sowing date and country in [âBAU-South-one Octâ âSh-North-five Juneâ âVT-South-thirty Octâ] and Max KWC >210.2 mg and KNPE >541, and (3) Sowing date and country in [âBAU-South-one Octâ âSh-North-five Juneâ âVT-South-thirty Octâ] and Max KWC >210.2 mg and Density p/ha>92500.</p
Traits involved in maize grain yield based on literature.
<p><b><sup>*</sup></b>RCBD: Randomized Complete Block Design.</p
The most important features involved in maize grain yield, selected by feature selection.
<p><sup>*</sup>Values closer to 1 show the higher importance.</p
Tree induced by decision tree algorithm with information gain ratio (L: less than 500 maize grain yield g m<sup>â2</sup>, M: 501â1000 maize grain yield g m<sup>â2</sup>, H: 1001â1500 maize grain yield g m<sup>â2</sup>, VH: more than 1500 maize grain yield g m<sup>â2</sup>, C: Clay, sandy clay).
<p>The tree shows that there is 2 pathways (routes) for reaching high yield according to this model (1) When âDuration of the grain filling periodâ>1127.5 and âSoil typeâ is Sandy clay, and (2) When âDuration of the grain filling periodâ>1127.5 and âstem dry weightâ>117.675.</p
Comparison of the filtering of dataset with feature selection algorithm based on K-Means model.
<p>(a) Most important generated cluster without feature selection filtering, cluster 4. (b) Most important generated cluster with feature selection filtering, cluster 3. 3. When K-Means model was applied on data filtered with feature selection, the records were put into 5 groups or clusters. When the model was applied on dataset without feature selection filtering, again five clusters were generated. In this clustering model, more than 28% of the records were put into the fourth cluster when the K-Means model was applied on the dataset without feature selection (Fig. 1a). When the K-Means model was applied on the dataset with feature selection filtering, more than 34% of the records were put into the third cluster (Fig. 1b). The number of iteration declined from 5 to 4 when feature selection applied on dataset.</p