Lex Fridman
Lex Fridman is an influencer

Cambridge, Massachusetts, United States Contact Info
2M followers 500+ connections

Join to view profile

About

Research in AI, human-robot interaction, autonomous vehicles, and machine learning at MIT.

Activity

Join now to see all activity

Experience & Education

  • Massachusetts Institute of Technology (MIT)

View Lex’s full experience

By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.

Publications

  • Active Authentication on Mobile Devices via Stylometry, GPS Location, Web Browsing Behavior, and Application Usage Patterns

    IEEE Systems Journal

    Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200subjects, each using their personal Android mobile device for a period of at least 30 days. This dataset is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The…

    Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200subjects, each using their personal Android mobile device for a period of at least 30 days. This dataset is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: (1) text entered via soft keyboard, (2) applications used, (3) websites visited, and (4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.

    See publication

More activity by Lex

View Lex’s full profile

  • See who you know in common
  • Get introduced
  • Contact Lex directly
Join to view full profile

Other similar profiles

Explore collaborative articles

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

Explore More

Others named Lex Fridman in United States

4 others named Lex Fridman in United States are on LinkedIn

See others named Lex Fridman

Add new skills with these courses

See all courses