Android platform has become a primary target for malware. In this paper we present SafeDroid, an open source distributed service to detect malicious apps on Android by combining static analysis and machine learning techniques. It is composed by three micro-services, working together, combining static analysis and machine learning techniques. SafeDroid has been designed as a user friendly service, providing detailed feedback in case of malware detection. The detection service is optimized to be lightweight and easily updated. The feature set on which the micro-service of detection relies on on has been selected and optimized in order to focus only on the most distinguishing characteristics of the Android apps. We present a prototype to show ...
Due to the increased number of mobile devices, they are integrated in every dimension of our daily l...
Anti-mobile malware has attracted the attention of the research and security community in recentyear...
In this paper, we propose a machine learning based approach to detect malicious mobile malware Andro...
Android smartphones have become a vital component of the daily routine of millions of people, runnin...
Android smartphones have become a vital component of the daily routine of millions of people, runnin...
Resource-constrained systems are becoming more and more common as users migrate from PCs to mobile d...
Mobile Phones have become an important need of today. The term mobile phone and smart phone are almo...
The prosperity of mobile devices have been rapidly and drastically reforming the use pattern and of ...
© 2018 Association for Computing Machinery. Android is the most popular mobile operating system havi...
The Android platform is the fastest growing market in smartphone operating systems to date. As such,...
Over the past few years, malware attacks have risen in huge numbers on the Android platform. Signifi...
Mobile malware has recently become an acute problem. Existing solutions either base static reasoning...
With the rapid growth of Android devices and applications, the Android environment faces more securi...
Abstract — The Android platform is the fastest growing market in smartphone operating systems to dat...
Currently, Android is the most popular operating system among mobile devices. However, as the number...
Due to the increased number of mobile devices, they are integrated in every dimension of our daily l...
Anti-mobile malware has attracted the attention of the research and security community in recentyear...
In this paper, we propose a machine learning based approach to detect malicious mobile malware Andro...
Android smartphones have become a vital component of the daily routine of millions of people, runnin...
Android smartphones have become a vital component of the daily routine of millions of people, runnin...
Resource-constrained systems are becoming more and more common as users migrate from PCs to mobile d...
Mobile Phones have become an important need of today. The term mobile phone and smart phone are almo...
The prosperity of mobile devices have been rapidly and drastically reforming the use pattern and of ...
© 2018 Association for Computing Machinery. Android is the most popular mobile operating system havi...
The Android platform is the fastest growing market in smartphone operating systems to date. As such,...
Over the past few years, malware attacks have risen in huge numbers on the Android platform. Signifi...
Mobile malware has recently become an acute problem. Existing solutions either base static reasoning...
With the rapid growth of Android devices and applications, the Android environment faces more securi...
Abstract — The Android platform is the fastest growing market in smartphone operating systems to dat...
Currently, Android is the most popular operating system among mobile devices. However, as the number...
Due to the increased number of mobile devices, they are integrated in every dimension of our daily l...
Anti-mobile malware has attracted the attention of the research and security community in recentyear...
In this paper, we propose a machine learning based approach to detect malicious mobile malware Andro...