Summary of Your Device May Know You Better Than You Know Yourself — Continuous Authentication on Novel Dataset Using Machine Learning, by Pedro Gomes Do Nascimento et al.
Your device may know you better than you know yourself – continuous authentication on novel dataset using machine learning
by Pedro Gomes do Nascimento, Pidge Witiak, Tucker MacCallum, Zachary Winterfeldt, Rushit Dave
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel dataset and machine learning models for continuous authentication using behavioral biometrics. The dataset consists of gesture data from 15 users playing Minecraft on a Samsung Tablet for 15 minutes each. The authors employed three machine learning binary classifiers – Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC) – to determine the authenticity of specific user actions. The most robust model was SVC, achieving an average accuracy of approximately 90%, demonstrating the effectiveness of touch dynamics in distinguishing users. However, further studies are needed to make it a viable option for authentication systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset and uses machine learning models to see if people can be identified by how they play Minecraft on a tablet. They collected data from 15 people playing for 15 minutes each. Then, they used three different computer programs – Random Forest, K-Nearest Neighbors, and Support Vector Classifier – to try to figure out who was doing what. The best one worked about 90% of the time! It looks like how we touch things on a screen can be used to tell us apart. But more work needs to be done before this can be used in real-life systems. |
Keywords
» Artificial intelligence » Machine learning » Random forest
