Abstract
Android malware applications and their detection have been under study by security experts for quite some time, but it gained special attention since the ever-growing use of smartphones. Normally, two methods have been commonly used for their identification. One, in which the code and information flow are analyzed is called the static analysis, whereas, in dynamic analysis, malware behaviour is over served at runtime (by executing it in a sandbox environment). It has been observed that both techniques when used separately, fail to identify all the malware, and, an analysis based on this, fail to achieve good accuracy. There is a need to make use of both these strategies for malware identification, so, if any malignant application identification fails during the static analysis, it gets caught during the dynamic one. Though researchers have used a combination of these two approaches and proposed different malware detection strategies, however, to the best of our knowledge none of them has examined the consent model associated with the applications intent in combination with others. Keeping this observation in mind, our proposed technique is a hybrid approach, based on applications intent, its permissions, static and dynamic data. Our supervised learning-based approach results have shown m 96% accuracy in detecting malware applications using gradient boosting classifier