MINDPRES: A Hybrid Prototype System for Comprehensive Data Protection in the User Layer of the Mobile Cloud

Area of Science:

• Computer Science
• Cybersecurity
• Mobile Computing

Background:

• Mobile cloud computing (MCC) enables services for mobile device (MD) users.
• Compromised MDs pose risks to users and other MCC customers.
• Existing security measures require enhancement for distributed MCC environments.

Purpose of the Study:

• To explore machine learning (ML) models and stochastic methods for securing Android MDs in MCC.
• To develop and validate a prototype system (MINDPRESS) for distributed security.
• To assess the feasibility, scalability, and performance of the proposed security framework.

Main Methods:

• Developed MINDPRESS, a prototype with static and hybrid components.
• Static component: Device-based ML for app risk assessment and cloud-based stochastic risk evaluation.
• Hybrid component: Real-time app behavior monitoring using ML models as an intrusion detection and prevention system (IDPS).

Main Results:

• MINDPRESS demonstrated accurate static and hybrid risk evaluation, comparable to recent studies.
• Performance evaluation confirmed the feasibility and scalability of the proposed framework.
• Power consumption analysis showed MINDPRESS operates without causing device overload.

Conclusions:

• MINDPRESS provides a feasible and scalable framework for protecting data and devices in MCC.
• The integration of ML and stochastic methods offers robust security for mobile cloud environments.
• This research contributes to the development of distributed security systems for mobile cloud users.