Leveraging the Effects of Cognitive Function on Input Device Analytics to Improve Security - January 2015

Public Audience
Purpose: To highlight project progress. Information is generally at a higher level which is accessible to the interested public. All information contained in the report (regions 1-3) is a Government Deliverable/CDRL.

PI(s):  David L. Roberts, Robert St. Amant
Researchers: Huseyin Sencan, Alok Goel

HARD PROBLEM(S) ADDRESSED

• Human Behavior - Our work addresses understanding human behavior through observations of input device usage. The basic principles we are developing will enable new avenues for characterizing risk and identifying malicious (or accidental) uses of systems that lead to security problems. The ultimate goal of our work is the development of a novel class of security proofs that we call "Human Sublety Proofs" (HSPs). HSPs combine the unobtrusiveness of Human Observational Proofs with the interactivity of Human Interactive Proofs, which hopefully will lead to more secure interactions.

PUBLICATIONS

None in this quarter

ACCOMPLISHMENT HIGHLIGHTS

• Developed a methodology for collecting data from users typing under a variety of cognitive conditions. Specifically, we have designed a typing game to collect data when participants have varying levels of familiarity with what they are typing, and engineered a way to scaffold muscle-memory learning to produce examples of various cognitive events without being overly-heavy-handed.
• Collected data from approximately 50 participants.
• Developed a cognitive model of transcription typing in the ACT-R cognitive framework. The new model is built off of an existing typing model that runs on an older cognitive architecture.