Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper, we proposed dynamic malware detection approach for Android applications. In dynamic analysis, system calls are recorded to calculate the density of the system calls. For density calculation, we used two different lengths of system calls that are 3 gram and 5 gram. Furthermore, Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3 gram Naive Bayes algorithm detects 84 malware application correctly and 14 benign application incorrectly. The 5 gram Naive Bayes algorithm detects 88 malware application correctly and 10 benign application incorrectly. Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdf