8 research outputs found

    Continuous authentication and its application in personal health record systems

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    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

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    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.

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    <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 [“AUS-North-ten May” “BA-South-fifteen Oct” “BI-North-eleven May” “PA-South-mid Sep” “Sh-North-fourteen June”] and KNPE >426 and Stem dry weight >122.478 and Mean KW >196.4 mg, (2) Sowing date and country in [“BAU-South-one Oct” “Sh-North-five June” “VT-South-thirty Oct”] and Max KWC >210.2 mg and KNPE >541, and (3) Sowing date and country in [“BAU-South-one Oct” “Sh-North-five June” “VT-South-thirty Oct”] and Max KWC >210.2 mg and Density p/ha>92500.</p

    Tree induced by decision tree algorithm with information gain ratio (L: less than 500 maize grain yield g m<sup>−2</sup>, M: 501–1000 maize grain yield g m<sup>−2</sup>, H: 1001–1500 maize grain yield g m<sup>−2</sup>, VH: more than 1500 maize grain yield g m<sup>−2</sup>, C: Clay, sandy clay).

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    <p>The tree shows that there is 2 pathways (routes) for reaching high yield according to this model (1) When “Duration of the grain filling period”>1127.5 and “Soil type” is Sandy clay, and (2) When “Duration of the grain filling period”>1127.5 and “stem dry weight”>117.675.</p

    Comparison of the filtering of dataset with feature selection algorithm based on K-Means model.

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    <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
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