A Risk Assessment Framework for Mobile Apps in Mobile Cloud Computing Environments

Noah Oghenefego Ogwara, Krassie Petrova (), Mee Loong Yang and Stephen G. MacDonell
Additional contact information
Noah Oghenefego Ogwara: Department of Computer Science and Software Engineering, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, City Campus, Auckland 1010, New Zealand
Krassie Petrova: Department of Computer Science and Software Engineering, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, City Campus, Auckland 1010, New Zealand
Mee Loong Yang: Department of Computer Science and Software Engineering, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, City Campus, Auckland 1010, New Zealand
Stephen G. MacDonell: Centre for Data Science and AI, School of Engineering and Computer Science, Faculty of Engineering, Victoria University of Technology, Kelburn Campus, Wellington 6012, New Zealand

Future Internet, 2024, vol. 16, issue 8, 1-17

Abstract: 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.

Keywords: mobile cloud computing; security threats; risk assessment; mobile device; mobile application; application security; data protection; machine learning; ensemble model (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/8/271/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/8/271/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:8:p:271-:d:1445556

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().