Mobile devices (MDs) are used by mobile cloud computing (MCC) customers and by other users because of their portability, robust connectivity, and ability to house and operate third-party applications (apps). However, the apps installed on an MD may pose data security risks to the MD owner and to other MCC users, especially when the requested permissions include access to sensitive data (e.g., user’s location and contacts). Calculating the risk score of an app or quantifying its potential harmfulness based on user input or on data gathered while the app is actually running may not provide reliable and sufficiently accurate results to avoid harmful consequences. This study develops and evaluates a risk assessment framework for Android-based MDs that does not depend on user input or on actual app behavior. Rather, an app risk evaluator assigns a risk category to each resident app based on the app’s classification (benign or malicious) and the app’s risk score. The app classifier (a trained machine learning model) evaluates the permissions and intents requested by the app. The app risk score is calculated by applying a probabilistic function based on the app’s use of a set of selected dangerous permissions. The results from testing of the framework on an MD with real-life resident apps indicated that the proposed security solution was effective and feasible.

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.

Mobile devices (MD) are used by mobile cloud computing (MCC) customers users because of their 11 portability and robust connectivity and the ability to house and operate third-party applications 12 (apps). However, the apps installed on a MD may pose data security risks to the MD owner and to 13 other MCC users, especially when the requested permissions include access to sensitive data (e.g., 14 user’s location and contacts). Calculating the risk score of an app or quantifying its potential 15 harmfulness based on user input or on data gathered while the app is actually running may not 16 provide reliable and sufficiently accurate results to avoid harmful consequences. This study 17 develops and evaluates a risk assessment framework for Android-based MDs that does not depend 18 on user input or on actual app behaviour. Rather, an app risk evaluator assigns a risk category to 19 reach resident app based on the app’s classification (benign or malicious) and the app’s risk score. 20 The app classifier (a trained machine learning model) considers the permissions and the intents 21 requested by the app. The apps risk score is calculated by a probabilistic function based on the 22 app’s use of a set of selected dangerous permissions. The results from the testing of an instance of 23 the framework on a MD with real-life resident apps indicated that the proposed security solution 24 was effective and feasible.

Mobile cloud computing (MCC) is a prevailing technological paradigm for providing services to customers who have access to mobile devices (MD). Security compromises at the user layer of MCC may cause harm to the MD user as well as to other MCC customers. This study proposes MINDPRES, a mobile app intrusion prevention and detection system to protect against malicious apps in the MD device. It applies machine learning models deployed on a cloud server to support the static analysis of the apps resident on the mobile device as well as performing dynamic monitoring of app activities and network traffic at runtime. The host-based MINDPRES manages the intrusion detection and protection process, including risk assessment and user communication. The paper also presents the outcomes of the testing of the prototype system on a set of real life benign and malicious apps, and compares its performance to results reported in the extant literature.