2008 TRUST Research Experiences for Undergraduates
Program Overview
The Team for Research in Ubiquitous Secure Technology sponsored ten undergraduate students to participate in the summer 2008 TRUST-REU program. Below are descriptions of the 2008 TRUST-REU research projects and links to each student's or team's research report, poster, and final presentation. .
Research Project
Nichole AlvarezPolytechnic University of Puerto Rico
Marjan Aslani
George Washington University
Nga Chung
San Jose State University
Jason Doherty
San Jose State University
Matiwos Gebre
Morgan State University
Shabana Khan
Cal Poly Pomona
Phoebe Lai
Lehigh University
William Quach
San Jose State University
Nichole Stockman
Mills College
Comparison of Blackbox and Whitebox Fuzzers in finding Software Vulnerabilities
Both blackbox and whitebox fuzzing techniques have been widely used to uncover security vulnerabilities in software applications, but there have been few studies comparing the efficiency of each technique. Our approach was to use Zzuf, a blackbox fuzzer, and Catchconv, a whitebox fuzzer, to generate test cases that were then run on open source and commercial software to compare both fuzzers' efficiency in terms of the number of unique bugs found per test case. An analysis of our results showed that Zzuf had a marginally higher average of unique errors per test case than Catchconv, while Catchconv had a higher percentage of unique errors per total errors found than Zzuf. We believe that since the statistics for both fuzzers are so close, a more accurate comparison of their performance is needed that will take into account the amount of CPU clock cycles required by each to generate a set amount of test cases. Doing so would allow us to compute the number of unique errors found by each fuzzer over a set amount of time. Nevertheless, without taking into account the amount of CPU clock cycles, Catchconv is slightly more efficient than Zzuf. Katherine Gilani
University of Dallas at Texas
Integration of Heterogeneous Motion Sensors and GPS in Healthcare Oriented Body Sensor Networks
In this paper, we propose and implement a mobile Body Sensor Network (BSN) that integrates a mobile device with wireless motion sensors and a positioning sensor such as GPS. This network significantly improves mobility by connecting the basestation to a mobile device carried by the user, whereas traditional BSN systems rely on using stationary base-stations that inherently limit the user's distance and functionality. The motion sensors worn on the body transmit data to the base-station, which supports persistent monitoring of human activities in both indoor and outdoor environments. In addition, we further provide the location information of the activities with the integration of GPS. We demonstrate the relevance of this system in healthcare-oriented applications. We particularly show that the system enhances the ability to monitor movements and their positions. Furthermore, it makes room for new functionalities such as air particle sensors to detect airborne particle matter (air pollution) encountered on a daily route. This work opens up an array of possibilities to the revolutionary alliance between BSNs and the healthcare industry.