Guessing Attacks on User-Generated Gesture Passwords
Abstract
Touchscreens, the dominant input type for mobile phones, require unique authentication solutions. Gesture passwords have been proposed as an alternative ubiquitous authentication technique. Prior security analysis has relied on inconsistent measurements such as mutual information or shoulder surfing attacks.We present the first approach for measuring the security of gestures with guessing attacks that model real-world attacker behavior. Our major contributions are: 1) a comprehensive analysis of the weak subspace for gesture passwords, 2) a method for enumerating the size of the full theoretical gesture password space, 3) a design of a novel guessing attack against user-chosen gestures using a dictionary, and 4) a brute-force attack used for benchmarking the performance of the guessing attack. Our dictionary attack, tested on newly collected user data, achieves a cracking rate of 47.71% after two weeks of computation using 109 guesses. This is a difference of 35.78 percentage points compared to the 11.93% cracking rate of the brute-force attack. In conclusion, users are not taking full advantage of the large theoretical password space and instead choose their gesture passwords from weak subspaces. We urge for further work on addressing this challenge.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 30, 2017
- Source ID
- 10.1145/3053331
Entities
People
- Can Liu
- Gradeigh D. Clark
- Janne Lindqvist
Organizations
- National Science Foundation
- Rutgers University