🔒 Christopher Grayson

🔒 Christopher Grayson

Los Angeles County, California, United States
2K followers 500+ connections

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South Bay Engineering

About

I am a self-starter and an avid computing enthusiast. I work best in open environments…

Activity

Experience

  • South Bay Engineering

    Manhattan Beach, California, United States

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    The Internet

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    Los Angeles County, California, United States

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    Playa Vista, California, United States

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    Santa Monica, California

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    Greater Los Angeles Area

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    Greater Atlanta Area

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    Greater Atlanta Area

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    Greater Atlanta Area

Education

Licenses & Certifications

Publications

  • Active Authentication Using Scrolling Behaviors

    International Conference on Information & Communication Systems 2015

    This paper addresses active authentication using scrolling behaviors for biometrics and assesses different classification and clustering methods that leverage those traits. The dataset used contained event-driven temporal data captured through monitoring users’ reading habits. The derived feature set is mainly composed of users’ scrolling events and their derivatives (changes) and 5-gram sequencing of scrolling events to increase the number of feature extracted and their context. Classification…

    This paper addresses active authentication using scrolling behaviors for biometrics and assesses different classification and clustering methods that leverage those traits. The dataset used contained event-driven temporal data captured through monitoring users’ reading habits. The derived feature set is mainly composed of users’ scrolling events and their derivatives (changes) and 5-gram sequencing of scrolling events to increase the number of feature extracted and their context. Classification performance in terms of both accuracy and Area under the Curve (AUC) for Receiver Operating Characteristic (ROC) curve is first reported using several classification methods including Random Forests (RF), RF with SMOTE (for unbalanced dataset) and AdaBoost with Decision Stump and ADTree. The best performance was obtained, however, using k-means clustering with two methods used to authenticate users: simple ranking and profile standard error filtering, with the latter achieving a success rate of 83.5%. Our use of k-means represents a novel non-intrusive approach of active and continuous re-authentication to counter insider-threat. Our main contribution comes from the features considered and their coupling to k-means to create a novel state-of-the art active user re-authentication method.

Courses

  • Applied Cryptography

    CS 6260

  • Compiler Design

    CS 6241

  • Computer Networking

    CS 6250

  • Introduction to Information Security

    CS 6035

  • Mobile and Cellular Security

    CS 8803

  • Network Security

    CS 6262

  • Secure Computer Systems

    CS 6238

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