Therefore, the data sources should be considered and their fusion preferably avoided while creating the training and testing data sets. We identified that dynamic features obtained for individual apps from different data sources (i.e., emulator and real device) show significant differences that can distort the modeling results. Additionally, this study sheds light on the usage of distinct data sources and their impact on concept drift modeling. Our experimental results show that a widely used timestamp in the literature yields poor results over time and that enhanced concept drift handling is achieved when an app internal timestamp was used. We evaluated and compared distinct timestamp alternatives. The relevant studies in the literature have not focused on the timestamp selection approach and its critical impact on effective drift modeling. In this study, we modeled the concept drift issue of Android malware detection, encompassing the years between 20, using dynamic feature sets (i.e., system calls) derived from Android apps. However, the vast majority of studies underestimate the evolving nature of the threat landscape, which requires the creation of a model life-cycle to ensure effective continuous detection in real-world settings over time. The large body of Android malware research has demonstrated that machine learning methods can provide high performance for detecting Android malware. Secondly, we have forecast Android market trends for the year up to 2018 and provide a unique hybrid security solution and take into account both the static and dynamic analysis an android application. We aim to provide an easy and concise view of the malware detection and protection mechanisms and deduce their benefits and limitations. We categorize many of the most recent antimalware techniques on the basis of their detection methods.

Firstly, we have analyzed the Android malwares and their penetration techniques used for attacking the systems and antivirus programs that act against malwares to protect Android systems.

In this paper, our contribution is twofold. There exist lots of antimalware programs which are designed to effectively protect the users’ sensitive data in mobile systems from such attacks. This rapidly increasing adoption of Android has resulted in significant increase in the number of malwares when compared with previous years. Android has become the most popular smartphone operating system.

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