Car recognition from frontal images in mobile environment

Author(s):  
Viktor Varjas ◽  
Attila Tanacs
Keyword(s):  
2009 ◽  
Vol 28 (10) ◽  
pp. 2544-2547
Author(s):  
Li-sheng CHEN ◽  
Hai-bin GUO ◽  
Fei-yue YE

2020 ◽  
Vol 10 (23) ◽  
pp. 8351
Author(s):  
Rosangela Casolare ◽  
Fabio Martinelli ◽  
Francesco Mercaldo ◽  
Antonella Santone

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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