Name
Abstract | ||
|---|---|---|
Existing researches on user mouse authentication mostly focus on fixed tracks, which leads to the lack of practicability. This paper is not restricted to fixed tracks and models the non-fixed mouse behavior pattern. By simulating scenarios of dynamic soft keyboard, mouse behavior data in relatively free tracks is collected. New mouse characteristics are proposed based on the behavioral trait. Mouse behavior feature vector is obtained by using a combination of Cumulative Distribution Function (CDF) and Plus-L Minus-R Selection (LRS). The Support Vector Machine (SVM) algorithm is adopted to build patterns, and the majority voting method is used for user authentication. Experimental results demonstrate the efficacy of the proposed method with a classification accuracy of 96.3%, which achieves a FAR of 1.98%, and a FRR of 2.10%. The proposed method can be adopted in non-fixed traces, which can be used as an assistant method for password authentication mechanism in real-world dynamic soft keyboard scenarios. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SMC.2016.7844243 | 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
Keywords | Field | DocType |
password authentication,SVM,support vector machine,LRS,plus-L minus-R selection,CDF,cumulative distribution function,mouse behavior feature vector,dynamic soft keyboard,mouse behavior authentication | Data collection,Behavioral pattern,Feature vector,Authentication,Computer science,Support vector machine,Cumulative distribution function,Artificial intelligence,Majority rule,Cybernetics,Machine learning | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-5090-1898-7 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Ma | 1 | 89 | 26.24 |
| Chun-Gang Yan | 2 | 62 | 15.97 |
| Peihai Zhao | 3 | 9 | 4.52 |
| Mimi Wang | 4 | 3 | 2.75 |