Thank you so much for being here.
The paper that we are going to present today is Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones.
Now we are introducing this paper from the following four aspects.
As we can see, people nowadays are arming their everyday life with smartphones, and those smart devices can be used for multiple tasks.
One direct result is that, there are private data that people do not want to compromise in them.
Then, how to secure these data becomes an important problem and has received widespread attention.
However, existing authentication mechanism demonstrated many problems.
To address these problems, implicit authentication comes with dedicated mechanism to deliver elevated performance.
Since the behavioral pattern of different users are unique, we can utilize this property to secure smartphones, so that even the graphic unlock pattern is leaked, data can stay safe and sound.
Although the above attempts have improved the security of traditional pattern lock, there are still remaining gaps hindering their real-world application.
The first point is that the fixed context assumed in those studies leads to a decreased performance during practical use.
The second point is existing studies ignores the use of fine-grained properties.
The third one is the assumption that the authentication model can prefetch the attacker’s data, which is clearly unreasonable.
To address G1, we introduce a context-aware module to achieve adaptive identity authentication.
To achieve the context-aware module, we have to go through the following workflow.
First, the sensor collects the behavior data and extracts the context features.
Then the context awareness module infers real-time context and passes this information to subsequent modules.
To address G2, we perform fine-grained analysis of users' unlocking behavior.
First, we propose a polyline weighted strategy to improve the authentication performance.
All polylines that make up the pattern lock are divided into 12 types of polyline basic units according to different angles.
We conduct experiments on each type of polyline basic unit to obtain the EER.
Considering the complexity and accuracy, we divide the authentication levels of polyline basic units into three categories: strong, medium and weak.
Compared with text-based passwords, consistency is an important feature of pattern.
Based on this characteristic, we propose a novel division rule.
Part of the line segment will be selected repeatedly.
Then, after the calculation of the score, we will then weight each segment to get the overall score.
Second, we propose two methods of behavioral information learning from raw data:
feature engineering-based way and CNN-based representation learning.
To address G3, we adopted OCSVM as the authentication module, so that the assumption on prefetching attacker’s data can be avoided.
In this way, the practicality of this approach can be enhanced.
The third part is performance evaluation.
In order to evaluate the mechanism more comprehensively, our paper conducts performance analysis from the overall level or sub-module level.
We compare the performance of our solution with previous studies.
As demonstrated in the table, experiments prove that our approach provides a better performance even in mixed conditions.
In addition to the overall performance comparison, we also verified the effectiveness of key modules.
For the context-aware module, we choose the best combination of sensors and classifiers.
Experimental results show that after the introduction of context awareness, the authentication performance is significantly improved.
We also tested the effectiveness of polyline weighted strategy.
The results show that it helps to improve the performance of the system, no matter which information learning method is deployed.
And this is the conclusion.
That’s all. Thank you.