Can's Projects

Selected Research Projects

Text Passwords Memorability

(Rutgers University, WINLAB, 2018)

Xianyi Gao, Yulong Yang, Can Liu, Christos Mitropoulos, Janne Lindqvist, and Antti Oulasvirta "Forgetting of Passwords: Ecological Theory and Data", USENIX Security '18.

Our work contributes new data and a set of analyses building on the ecological theory of memory and forgetting. We propose that human memory naturally adapts according to an estimate of how often a password will be needed, such that often used, important passwords are less likely to be forgotten. We derive models for login duration and odds of recall as a function of rate of use and number of uses thus far. The models achieved a root-mean-square error (RMSE) of 1.8 seconds for login duration and 0.09 for recall odds for data collected in a month-long field experiment where frequency of password use was controlled. The theory and data shed new light on password management, account usage, password security and memorability.

Guessing Attack on Gesture Authentication System

(Rutgers University, 2016)

Can Liu, Gradeigh D. Clark, and Janne Lindqvist, "Guessing Attacks on User-Generated Gesture Passwords", IMWUT Issue 1, Article 3 (March 2017)

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 achieves a cracking rate of 47.71% using 10^9 guesses. This is a difference of 35.78 percentage points compared to the 11.93% cracking rate of the brute-force attack.

Gesture Authentication System on Mobile Devices

(Rutgers University, WINLAB, 2015)

Can Liu, Gradeigh D. Clark and Janne Lindqvist, 2017, "Demo: Garda - Robust Gesture-based Authentication for Mobile Systems", UbiComp '17

Can Liu, Gradeigh D. Clark, and Janne Lindqvist, "Where Usability and Security Go Hand-in-Hand: Robust Gesture-Based Authentication for Mobile Systems", CHI '17

We presented and evaluated a novel multi-expert gesture recognizer design for authentication: Garda. We also implemented and evaluated Garda on a mobile device. All our results show that our implementation can largely improve the performance of gesture-based authentication systems. Garda was the final result of a rigorous evaluation of 13 different methods to implement gesture recognizers. Finally, we conducted the first analysis of how tuning the variables of preprocessing methods of gesture recognizers can impact their authentication performance. We found that an authentication-optimal combination (location invariant, scale variant, and rotation variant) can reduce up to 45.3% of EER on average compared to recognition-optimal configuration used in previous work.

YouTube

Monitoring System of Residual Current Devices (RCD)

(Hebei University of Technology, State Key Lab of Reliability and Intelligence of Electrical Equipment, 2012)

Can Liu, Kui Li, Ning Zhang, Yao Wang, "Intelligent Data Management and Monitoring System of Residual Current Device Based on LabVIEW", Low-Voltage Apparatus, 2012

Ning Zhang, Kui Li, Can Liu, Simin Chen, "Residual Current Protection Device Monitoring System Based on LabVIEW", Low-Voltage Apparatus, 2012

With LabVIEW, we developed a monitor system for RCDs with powerful functions and stable performance. The system has the visible and easily operated interface. It can monitor the structure and operational parameters of the breakers at all levels. The fully functional database can achieve the function of storing ,inquiring and printing the history monitoring data.

Research on Residual Current Identification under Complex Conditions

(Hebei University of Technology, State Key Lab of Reliability and Intelligence of Electrical Equipment, 2011)

Yao Wang, Kui Li, Zhitao Guo, Can Liu, etc, "Development of an AC-DC Sensitive Residual Current Device", Low-Voltage Apparatus, 2013

Can Liu, Kui Li, Yao Wang, Xu Zou, etc, "Comparative Study on Residual Current Signal Processing Method of AC/DC Sensitive Residual Current Transformer", Asia-Pacific Power and Energy Engineering Conference, China, 2012

Yao Wang, Kui Li, Can Liu, Zhitao Guo, etc, "Study on Modeling and Simulation of AC/DC Sensitive Residual Current Transformer", the 1st International Conference on Electric Power Equipment-Switching Technology, China, 2011

We proposed an AC/DC RCT based on magnetic modulation principle. We modeled and simulated the AC/DC RCT based on linear regression. We analyzed the operation principle of AC/DC RCT and built a computer based simulation model. Finally, the validity of the simulation model is verified by experiment. Our work will help the design of AC/DC RCT.

We also studied five typical residual current signals, which include full-wave, half-wave, quarter-wave, and 135 degree wave signals. We also compared three approaches to process the residual current signals and found FIR low-pass digital filter is optimal for the signal processing of AC/DC sensitive residual current transformer.

Selected Course Projects

Coming Soon...