In this paper, we present a smart phone dual defense protection framework that allows
Official and Alternative Android Markets to detect malicious applications among those new …

The widespread adoption and contextually sensitive nature of smartphone devices has
increased concerns over smartphone malware. Machine learning classifiers are a current …

As mobile devices have supported various services and contents, much personal
information such as private SMS messages, bank account information, etc. is scattered in …

In this paper, we propose An Android Behavior-Based Malware Detection Method using
Machine Learning. We improve an Android application sandbox, Droidbox, by inserting a …

Malware detection is one of the most important factors in the security of smartphones.
Academic researchers have extensively studied Android malware detection problems …

Mobile malware is so pernicious and on the rise, accordingly having a fast and reliable
detection system is necessary for the users. In this research, a new detection and …

Malware for smart phones is rapidly spreading out. This paper proposes a method for
detecting malware based on three metrics, which evaluate: the occurrences of a specific …

We present a new method of screening for malicious Android applications that uses two
types of information about the application: the permissions that the application requests in its …

In this technology world, smartphones are greatly adopted by people due to the need of
personal communication, Internet and many more requirements. Users are attracted to use …

With the increase in mobile device use, there is a greater need for increasingly sophisticated
malware detection algorithms. The research presented in this paper examines two types of …