An approach for an adaptive visualization in a mobile environment

1997 ◽  
pp. 272-282 ◽  
Author(s):  
Luc Neumann ◽  
Alberto B. Raposo
Keyword(s):  
Mobile Environment ◽  
Adaptive Visualization

Synchronization and replication model in mobile environment

2009 ◽  
Vol 28 (10) ◽  
pp. 2544-2547
Author(s):  
Li-sheng CHEN ◽  
Hai-bin GUO ◽  
Fei-yue YE
Keyword(s):  
Mobile Environment ◽  
Replication Model

Database and Caching Support for Adaptive Visualization of Large Sensor Data

2020 ◽  
Author(s):  
Sapan Tanted ◽  
Anshul Agarwal ◽  
Shinjan Mitra ◽  
Chaitra Bahuman ◽  
Krithi Ramamritham
Keyword(s):  
Sensor Data ◽  
Adaptive Visualization

scholarly journals Detecting Colluding Inter-App Communication in Mobile Environment

2020 ◽  
Vol 10 (23) ◽  
pp. 8351
Author(s):  
Rosangela Casolare ◽  
Fabio Martinelli ◽  
Francesco Mercaldo ◽  
Antonella Santone
Keyword(s):  
Model Checking ◽  
Mobile Devices ◽  
Private Information ◽  
End Users ◽  
Home Automation ◽  
Mobile Environment ◽  
Data Support ◽  
Bank Transfer ◽  
Android Applications

The increase in computing capabilities of mobile devices has, in the last few years, made possible a plethora of complex operations performed from smartphones and tablets end users, for instance, from a bank transfer to the full management of home automation. Clearly, in this context, the detection of malicious applications is a critical and challenging task, especially considering that the user is often totally unaware of the behavior of the applications installed on their device. In this paper, we propose a method to detect inter-app communication i.e., a colluding communication between different applications with data support to silently exfiltrate sensitive and private information. We based the proposed method on model checking, by representing Android applications in terms of automata and by proposing a set of logic properties to reduce the number of comparisons and a set of logic properties automatically generated for detecting colluding applications. We evaluated the proposed method on a set of 1092 Android applications, including different colluding attacks, by obtaining an accuracy of 1, showing the effectiveness of the proposed method.

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