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We reported equal error rate for both the Liquid State Machine extracted feature vector and best-reported feature vector from the literature
Toward a continuous authentication system using a biologically inspired machine learning approach - a case study.
SAC, pp.1362-1364, (2019)
Smartphones have recently seen massive growth in usage and become a repository for many types of personal information. The privacy and security are primary concerns for their usage, where there is a need to provide seamless and continuous authentication systems(CASs) for smartphones. We introduce in this work a proof-of-concept design and...More
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- The market analysis predicts that in 2020 there will be 6.1 billion smartphones in use worldwide 
- We reported equal error rate (EER) for both the Liquid State Machine (LSM) extracted feature vector and best-reported feature vector from the literature
- This provides more information about how informative the features extracted by LSM are
- For long-text and stroke/gesture authentication, we reported EER values for only LSM-extracted feature vector
- This work provided a proof-of-concept of using hybrid LSM as a CAS
- We showed that the extracted features from LSM are more discriminative than the ones reported in the literature for augmented password authentication Table 2 and 3, while achieving low EER for other types of interactions Table 4, 5, 6 and 7
- Table1: Input types and mappings to aLSM and sLSM
- Table2: Augmented Password EER (One vs. One scenario)
- Table3: Augmented Password EER (One vs. All)
- Table4: Long-Text EER (One vs. One)
- Table5: Long-Text EER (One vs. All)
- Table6: Gestures/Strokes EER (One vs. One)
- Table7: Gestures/Strokes EER (One vs. All)
- The authors thank the Research Board at the American University of Beirut for supporting this work
Study subjects and analysis
To do this, first, we designed an Android application to collect and log user interactions with a smartphone. The application was installed on a Samsung GT-i9100 smartphone, and we asked 22 users to participate in data collection. All users are students between 20 to 28 years of age (9 males)
The application was installed on a Samsung GT-i9100 smartphone, and we asked 22 users to participate in data collection. All users are students between 20 to 28 years of age (9 males). Each user was asked to perform a 3-step data collection
The third step included data collection for strokes/gestures, where the users were asked to swipe anywhere on the screen in a specific direction according to a label that appears on the smartphone’s touchscreen. We tested the framework on the 22 users who participated in the data collection. We reported the results separately for the three types of interactions: augmented password, long-text, and stroke/gesture authentication
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