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Utilization of Internet in everyday life has made us vulnerable in terms of security and privacy of our data and systems. For example, large-scale data breaches have occurred at Yahoo and Equifax because of lacking of robust and secure data protection within systems. Therefore, it is imperative to find solutions to further boost data security and protect privacy of our systems. To this end, we propose to authenticate users by utilizing score-level fusions based on mouse dynamics (e.g., mouse movement on a screen) and widget interactions (e.g., when clicking or hovering over different icons on a screen) on two novel datasets. In this study, we focus on two common applications, PayPal (a money transaction website) and Facebook (a social media platform). Though we fuse the same modalities for both applications, the purpose of investigating PayPal is to demonstrate how we can authenticate users when the users interact with the app for only a short period of time, while the purpose of investigating Facebook is to authenticate users based on social media browsing activities. We have a total of 10 users for PayPal with an average of 12 minutes of data per user and a total of 15 users for Facebook with an average of 2 hours of data per user. By fusing a single mouse trajectory with the associated widget interactions that occur during the trajectory, our mean EERs (Equal Error Rates) with a score-level fusion of mouse dynamics and widget interactions are 7.64% (SVM-rbf) and 3.25% (GBM), for PayPal, and 5.49% (SVM-rbf) and 2.54% (GBM), for Facebook. To further improve the performance of our fusion, we combine decision scores from multiple consecutive trajectories, which yields a 0% mean EER after 11 decision scores across all the users for both PayPal and Facebook.
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Authenticating Facebook Users Based on Widget Interaction Behavior
Facebook has become an important part of our daily life. From knowing the status of our relatives, showing off a new car, to connecting with a high school classmate, abundant personally identifiable information (PII) are made visible to others by posts, images and news. However, this free flow of information has also created significant cyber-security challenges that make us vulnerable to social engineering and cyber crimes. To confront these challenges, we propose a new behavioral biometric that verifies a user based on his or her widget interaction behavior when using Facebook. Specifically, we monitor activities on the user’s Facebook account using our own logging software and verify the user’s claimed identity by binary classifiers trained with two algorithms (SVM-rbf and the GBM– Gradient Boosting Machines). Our novel dataset consists of eight users over a month of data collection with an average of 2.95k rows of data per user. We convert these activities data into meaningful features such as day-of-week, hour-of-day, and widget types and duration of mouse staying on a widget. The performance shows that our novel widget interaction modality is promising for authentication. The SVM-rbf classifiers achieve a mean Equal Error Rate (EER) and mean Accuracy (ACC) of 3.91% and 97.79%, while the GBM classifiers a mean EER and ACC of 2.76% and 97.88%, respectively. In addition, we perform an ablation study to understand the impact of individual features on authentication performance. The importance of features are ranked in the descending order of hour-of-day, day-of-week, and widget types and duration.
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- Award ID(s):
- 1650503
- PAR ID:
- 10323031
- Date Published:
- Journal Name:
- 2021 IEEE 18th Annual Consumer Communications & Networking Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCNC49032.2021.9369604
