Name
Title | ||
|---|---|---|
Touch-Based Active Cloud Authentication Using Traditional Machine Learning And Lstm On A Distributed Tensorflow Framework |
Abstract | ||
|---|---|---|
In this modern world, mobile devices have been paired with the cloud environment to scale the voluminous amount of generated data. The implementation comes at the cost of privacy as proprietary data can be stolen in transit to the cloud, or victims' phones can be seized along with synced data from cloud. The attacker can gain access to the phone through shoulder surfing, or even spoofing attacks. Our approach is to mitigate this issue by proposing an active cloud authentication framework using touch biometric pattern. To the best of our knowledge, active cloud authentication using touch dynamics for mobile cloud computing has not been explored in the literature. This research creates a proof of concept that will lead into a simulated cloud framework for active authentication. Given the amount of data captured by the mobile device from user activity, it can be a computationally intensive process for the mobile device to handle with such limited resources. To solve this, we simulated a post-transmission process of data to the cloud so that we could implement the authentication process within the cloud. We evaluated the touch data using traditional machine learning algorithms, such as Random Forest (RF), Support Vector Machine (SVM), and also using a deep learning classifier, the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) algorithms. The novelty of this work is twofold. First, we develop a distributed tensorflow framework for cloud authentication using touch biometric pattern. This framework helps alleviate the drawback of the computationally intensive recognition of the substantial amount of raw data from the user. Second, we apply the RF, SVM, and a deep learning classifier, the LSTM-RNN, on the touch data to evaluate the performance of the proposed authentication scheme. The proposed approach shows a promising performance with an accuracy of 99.0361% using RF on the distributed tensorflow framework. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1142/S1469026819500226 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
Keywords | Field | DocType |
Active authentication, behavioral biometrics, touch pattern, distributed tensorflow, mobile cloud computing | Authentication,Computer science,Artificial intelligence,Machine learning,Cloud computing | Journal |
Volume | Issue | ISSN |
18 | 4 | 1469-0268 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dylan J. Gunn | 1 | 1 | 0.35 |
| Zhipeng Liu | 2 | 1 | 1.36 |
| Rushit Dave | 3 | 1 | 0.35 |
| Xiaohong Yuan | 4 | 169 | 26.72 |
| Kaushik Roy | 5 | 4 | 3.15 |