An Adaptive Context-Aware Authentication System on Smartphones Using Machine Learning

Abstract

The authentication method for unlocking screens is an essential security feature of smartphones to avoid unauthorized access. Various mechanisms have been integrated into smartphones to authenticate users to reduce privacy infringement. Contextual awareness is now a key requirement for mobile computing to make intelligent decisions and provide an adaptable and convenient authentication model. Therefore, in this paper, a context-aware authentication system based on a machine learning technique is proposed. The proposed system takes the user’s body postures, location, SMS contents, and ambient environmental conditions as context. To enhance privacy protection, these context factors are selected to guide the authentication process and enable the authentication system to understand users and their surrounding environment. However, to provide the most appropriate authentication method, a machine learning model is designed. The model is trained and tested with the user’s context datasets. An appropriate dataset for the machine learning model is generated, and features that affect end-user interaction during the authentication process are identified. A total of 25 responses from smartphone owners were collected to evaluate the proposed system. After conducting a sentiment analysis, we found that 72 percent of users have a positive sentiment regarding the proposed system, which means that context-aware technology helps improve authentication adaptability and provides a convenient authentication method. The performance of the model was tested, and the results show that the proposed model effectively achieves a Mean Absolute Error (MAE) of 1.299, a Root Mean Square Error (RMSE) of 1.437, and an R Square of 76.78. Therefore, the system can improve the reliability and adaptability of the authentication process.