| Autor: |
Noah Oghenefego Ogwara, Krassie Petrova, Mee Loong Yang, Stephen G. MacDonell |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Future Internet, Vol 16, Iss 8, p 271 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1999-5903 |
| DOI: |
10.3390/fi16080271 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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