Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Area of Science:

• Computer Science
• Cybersecurity
• Human-Computer Interaction

Background:

• Continuous authentication on mobile devices aims to improve security and user experience by recognizing user behavior patterns.
• Existing systems face challenges in authentication accuracy, adaptability to user behavior changes, and resource consumption (battery, computation, response time).

Purpose of the Study:

• To design and implement an intelligent and adaptive continuous authentication system for mobile devices.
• To address challenges in accuracy, adaptability, and resource efficiency for mobile continuous authentication.

Main Methods:

• The system utilizes statistical information from applications and sensors.
• Machine learning techniques, specifically anomaly detection, are employed for real-time user authentication.
• The system is designed to be adaptive to evolving user behaviors.

Main Results:

• Experiments demonstrated the proposed system's accuracy and adaptability.
• The solution showed efficient resource consumption, considering mobile device limitations.
• Real-time authentication capabilities were successfully implemented.

Conclusions:

• The developed intelligent and adaptive continuous authentication system effectively enhances mobile security and user experience.
• The system's practical utility is validated through a proof-of-concept online bank application, enabling context-aware actions based on authentication levels.