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

Abstract

Mobile cloud computing (MCC) is a technological paradigm for providing services to mobile device (MD) users. A compromised MD may cause harm to both its user and to other MCC customers. This study explores the use of machine learning (ML) models and stochastic methods for the protection of Android MDs connected to the mobile cloud. To test the validity and feasibility of the proposed models and methods, the study adopted a proof-of-concept approach and developed a prototype system named MINDPRESS. The static component of MINDPRES assesses the risk of the apps installed on the MD. It uses a device-based ML model for static feature analysis and a cloud-based stochastic risk evaluator. The device-based hybrid component of MINDPRES monitors app behavior in real time. It deploys two ML models and functions as an intrusion detection and prevention system (IDPS). The performance evaluation results of the prototype showed that the accuracy achieved by the methods for static and hybrid risk evaluation compared well with results reported in recent work. Power consumption data indicated that MINDPRES did not create an overload. This study contributes a feasible and scalable framework for building distributed systems for the protection of the data and devices of MCC customers.